Instruction: Memory limits exceeded

Output: Try increasing --num-cpus in ray start command.


Instruction: Dependency conflicts

Output: Try increasing --num-cpus in ray start command.


Instruction: Resource exhaustion

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Model inference too slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Kubernetes pods not starting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Ray cluster won't start

Output: Try increasing --num-cpus in ray start command.


Instruction: API rate limiting

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Resource exhaustion

Output: Try increasing --num-cpus in ray start command.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Service discovery issues

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Container build failures

Output: Core principle + practical example for implementation.


Instruction: Pipeline performance issues

Output: Here's the solution without asking any clarifying questions.


Instruction: Tests failing randomly

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Resource exhaustion

Output: Here's the solution without asking any clarifying questions.


Instruction: Memory limits exceeded

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray cluster won't start

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Ray cluster won't start

Output: Core principle + practical example for implementation.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Tests failing randomly

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray Dataset processing is slow

Output: Try increasing --num-cpus in ray start command.


Instruction: Service returns 500 errors

Output: Add an index on the primary key to fix the issue.


Instruction: Memory limits exceeded

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Dependency conflicts

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray cluster won't start

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Network timeouts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Service returns 500 errors

Output: Add an index on the primary key to fix the issue.


Instruction: Ray cluster won't start

Output: Try increasing --num-cpus in ray start command.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Configuration not loading

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Ray Dataset processing is slow

Output: Try increasing --num-cpus in ray start command.


Instruction: Service returns 500 errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Pipeline performance issues

Output: Add an index on the primary key to fix the issue.


Instruction: Pipeline performance issues

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Ray Dataset processing is slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Load balancing problems

Output: Core principle + practical example for implementation.


Instruction: Configuration not loading

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray Dataset processing is slow

Output: Here's the solution without asking any clarifying questions.


Instruction: Tests failing randomly

Output: Core principle + practical example for implementation.


Instruction: Container build failures

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Service discovery issues

Output: Here's the solution without asking any clarifying questions.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Model inference too slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Dependency conflicts

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Container build failures

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Tests failing randomly

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Resource exhaustion

Output: Core principle + practical example for implementation.


Instruction: Dependency conflicts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: File permission errors

Output: Add an index on the primary key to fix the issue.


Instruction: Load balancing problems

Output: Add an index on the primary key to fix the issue.


Instruction: Service returns 500 errors

Output: Core principle + practical example for implementation.


Instruction: Authentication failing

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Dependency conflicts

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Kubernetes pods not starting

Output: Core principle + practical example for implementation.


Instruction: Resource exhaustion

Output: Add an index on the primary key to fix the issue.


Instruction: Resource exhaustion

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Load balancing problems

Output: Here's the solution without asking any clarifying questions.


Instruction: Kubernetes pods not starting

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Dependency conflicts

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Tests failing randomly

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Network timeouts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Kubernetes pods not starting

Output: Core principle + practical example for implementation.


Instruction: Authentication failing

Output: Try increasing --num-cpus in ray start command.


Instruction: Service discovery issues

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Resource exhaustion

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Ray Dataset processing is slow

Output: Try increasing --num-cpus in ray start command.


Instruction: Resource exhaustion

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Load balancing problems

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Dependency conflicts

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Resource exhaustion

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray Dataset processing is slow

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: API rate limiting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Resource exhaustion

Output: Try increasing --num-cpus in ray start command.


Instruction: Network timeouts

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Load balancing problems

Output: Here's the solution without asking any clarifying questions.


Instruction: Load balancing problems

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Dependency conflicts

Output: Try increasing --num-cpus in ray start command.


Instruction: Dependency conflicts

Output: Core principle + practical example for implementation.


Instruction: Dependency conflicts

Output: Core principle + practical example for implementation.


Instruction: Service returns 500 errors

Output: Add an index on the primary key to fix the issue.


Instruction: Network timeouts

Output: Core principle + practical example for implementation.


Instruction: Network timeouts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Load balancing problems

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Container build failures

Output: Here's the solution without asking any clarifying questions.


Instruction: Network timeouts

Output: Try increasing --num-cpus in ray start command.


Instruction: Service discovery issues

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Network timeouts

Output: Add an index on the primary key to fix the issue.


Instruction: Kubernetes pods not starting

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Load balancing problems

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: File permission errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Memory limits exceeded

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: File permission errors

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Tests failing randomly

Output: Here's the solution without asking any clarifying questions.


Instruction: API rate limiting

Output: Add an index on the primary key to fix the issue.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Tests failing randomly

Output: Core principle + practical example for implementation.


Instruction: Model inference too slow

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray Dataset processing is slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Container build failures

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Tests failing randomly

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Network timeouts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Authentication failing

Output: Add an index on the primary key to fix the issue.


Instruction: Container build failures

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Kubernetes pods not starting

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Service returns 500 errors

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: API rate limiting

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray cluster won't start

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Ray Dataset processing is slow

Output: Core principle + practical example for implementation.


Instruction: Pipeline performance issues

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: API rate limiting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Authentication failing

Output: Add an index on the primary key to fix the issue.


Instruction: Database is slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Authentication failing

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: File permission errors

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Container build failures

Output: Here's the solution without asking any clarifying questions.


Instruction: Configuration not loading

Output: Here's the solution without asking any clarifying questions.


Instruction: File permission errors

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Network timeouts

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Pipeline performance issues

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Kubernetes pods not starting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Container build failures

Output: Add an index on the primary key to fix the issue.


Instruction: Database is slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Network timeouts

Output: Core principle + practical example for implementation.


Instruction: Dependency conflicts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Resource exhaustion

Output: Core principle + practical example for implementation.


Instruction: Resource exhaustion

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Ray Dataset processing is slow

Output: Here's the solution without asking any clarifying questions.


Instruction: Service returns 500 errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Tests failing randomly

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray cluster won't start

Output: Add an index on the primary key to fix the issue.


Instruction: API rate limiting

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Pipeline performance issues

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: Ray Dataset processing is slow

Output: Core principle + practical example for implementation.


Instruction: Pipeline performance issues

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Service returns 500 errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: GPU training crashes immediately

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Network timeouts

Output: Add an index on the primary key to fix the issue.


Instruction: Database is slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray cluster won't start

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Database is slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Ray cluster won't start

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Ray cluster won't start

Output: Here's the solution without asking any clarifying questions.


Instruction: Configuration not loading

Output: Core principle + practical example for implementation.


Instruction: Database is slow

Output: Add an index on the primary key to fix the issue.


Instruction: Ray cluster won't start

Output: Add an index on the primary key to fix the issue.


Instruction: Resource exhaustion

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Dependency conflicts

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Kubernetes pods not starting

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Dependency conflicts

Output: Try increasing --num-cpus in ray start command.


Instruction: Service returns 500 errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Service returns 500 errors

Output: Here's the solution without asking any clarifying questions.


Instruction: Authentication failing

Output: Add an index on the primary key to fix the issue.


Instruction: Resource exhaustion

Output: Core principle + practical example for implementation.


Instruction: Memory limits exceeded

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Pipeline performance issues

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: File permission errors

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Pipeline performance issues

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Resource exhaustion

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Ray Dataset processing is slow

Output: Here's the solution without asking any clarifying questions.


Instruction: File permission errors

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Pipeline performance issues

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Memory limits exceeded

Output: Try increasing --num-cpus in ray start command.


Instruction: Database is slow

Output: Try increasing --num-cpus in ray start command.


Instruction: Tests failing randomly

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Ray cluster won't start

Output: Here's the solution without asking any clarifying questions.


Instruction: API rate limiting

Output: Core principle + practical example for implementation.


Instruction: Ray Dataset processing is slow

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: Dependency conflicts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Resource exhaustion

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output: Add an index on the primary key to fix the issue.


Instruction: Kubernetes pods not starting

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Dependency conflicts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Ray Dataset processing is slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Container build failures

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Service discovery issues

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Container build failures

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: Load balancing problems

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Dependency conflicts

Output: Add an index on the primary key to fix the issue.


Instruction: GPU training crashes immediately

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Kubernetes pods not starting

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Service returns 500 errors

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Dependency conflicts

Output: Try increasing --num-cpus in ray start command.


Instruction: Load balancing problems

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Authentication failing

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Container build failures

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Dependency conflicts

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: API rate limiting

Output: Add an index on the primary key to fix the issue.


Instruction: Authentication failing

Output: Try increasing --num-cpus in ray start command.


Instruction: Resource exhaustion

Output: Add an index on the primary key to fix the issue.


Instruction: Tests failing randomly

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray Dataset processing is slow

Output: Add an index on the primary key to fix the issue.


Instruction: Dependency conflicts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: API rate limiting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: File permission errors

Output: Core principle + practical example for implementation.


Instruction: Service discovery issues

Output: Try increasing --num-cpus in ray start command.


Instruction: Dependency conflicts

Output: Core principle + practical example for implementation.


Instruction: Ray cluster won't start

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Load balancing problems

Output: Add an index on the primary key to fix the issue.


Instruction: Ray cluster won't start

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Database is slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray Dataset processing is slow

Output:
Gather evidence:
1. Performance metrics: CPU, memory, I/O
2. Query execution plans or profiling data
3. Resource utilization patterns
4. Network latency measurements

Diagnose only after 2+ signals align.


Instruction: Ray cluster won't start

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Tests failing randomly

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Container build failures

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: GPU training crashes immediately

Output: Add an index on the primary key to fix the issue.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: Service discovery issues

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Model inference too slow

Output: Add an index on the primary key to fix the issue.


Instruction: Database is slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: API rate limiting

Output: Add an index on the primary key to fix the issue.


Instruction: Ray Dataset processing is slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Load balancing problems

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Network timeouts

Output: Core principle + practical example for implementation.


Instruction: Model inference too slow

Output: Here's the solution without asking any clarifying questions.


Instruction: Kubernetes pods not starting

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Container build failures

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: File permission errors

Output: Add an index on the primary key to fix the issue.


Instruction: Configuration not loading

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Memory limits exceeded

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: API rate limiting

Output: Try increasing --num-cpus in ray start command.


Instruction: Configuration not loading

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Database is slow

Output:
Gather evidence:
1. Performance metrics: CPU, memory, I/O
2. Query execution plans or profiling data
3. Resource utilization patterns
4. Network latency measurements

Diagnose only after 2+ signals align.


Instruction: Authentication failing

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: API rate limiting

Output: Here's the solution without asking any clarifying questions.


Instruction: Tests failing randomly

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Load balancing problems

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: API rate limiting

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Container build failures

Output: Add an index on the primary key to fix the issue.


Instruction: Container build failures

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Container build failures

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Memory limits exceeded

Output: Try increasing --num-cpus in ray start command.


Instruction: API rate limiting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Model inference too slow

Output:
Gather evidence:
1. Performance metrics: CPU, memory, I/O
2. Query execution plans or profiling data
3. Resource utilization patterns
4. Network latency measurements

Diagnose only after 2+ signals align.


Instruction: Ray cluster won't start

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Container build failures

Output: Core principle + practical example for implementation.


Instruction: Service returns 500 errors

Output: Try increasing --num-cpus in ray start command.


Instruction: Service discovery issues

Output: Try increasing --num-cpus in ray start command.


Instruction: Dependency conflicts

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Ray cluster won't start

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Container build failures

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Dependency conflicts

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: File permission errors

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Service returns 500 errors

Output: Here's the solution without asking any clarifying questions.


Instruction: Load balancing problems

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Load balancing problems

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Kubernetes pods not starting

Output: Add an index on the primary key to fix the issue.


Instruction: Model inference too slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Database is slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Resource exhaustion

Output: Here's the solution without asking any clarifying questions.


Instruction: API rate limiting

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Resource exhaustion

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: GPU training crashes immediately

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Authentication failing

Output: Add an index on the primary key to fix the issue.


Instruction: Service discovery issues

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Resource exhaustion

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Network timeouts

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Resource exhaustion

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray cluster won't start

Output: Add an index on the primary key to fix the issue.


Instruction: Kubernetes pods not starting

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: Memory limits exceeded

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Kubernetes pods not starting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Service discovery issues

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Tests failing randomly

Output: Core principle + practical example for implementation.


Instruction: Service discovery issues

Output: Try increasing --num-cpus in ray start command.


Instruction: Authentication failing

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Authentication failing

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Service discovery issues

Output: Try increasing --num-cpus in ray start command.


Instruction: Container build failures

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output: Here's the solution without asking any clarifying questions.


Instruction: Pipeline performance issues

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Authentication failing

Output: Try increasing --num-cpus in ray start command.


Instruction: Kubernetes pods not starting

Output: Try increasing --num-cpus in ray start command.


Instruction: Load balancing problems

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray cluster won't start

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Authentication failing

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray Dataset processing is slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Kubernetes pods not starting

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: GPU training crashes immediately

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Authentication failing

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Model inference too slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Pipeline performance issues

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Service discovery issues

Output: Here's the solution without asking any clarifying questions.


Instruction: Memory limits exceeded

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Model inference too slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Network timeouts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: File permission errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output: Core principle + practical example for implementation.


Instruction: Kubernetes pods not starting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Model inference too slow

Output: Here's the solution without asking any clarifying questions.


Instruction: Container build failures

Output: Try increasing --num-cpus in ray start command.


Instruction: Tests failing randomly

Output: Core principle + practical example for implementation.


Instruction: Container build failures

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Pipeline performance issues

Output: Add an index on the primary key to fix the issue.


Instruction: Container build failures

Output: Core principle + practical example for implementation.


Instruction: Load balancing problems

Output: Here's the solution without asking any clarifying questions.


Instruction: Database is slow

Output: Try increasing --num-cpus in ray start command.


Instruction: Kubernetes pods not starting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Ray cluster won't start

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Database is slow

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray cluster won't start

Output: Core principle + practical example for implementation.


Instruction: Load balancing problems

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Kubernetes pods not starting

Output: Add an index on the primary key to fix the issue.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: API rate limiting

Output: Add an index on the primary key to fix the issue.


Instruction: Kubernetes pods not starting

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Resource exhaustion

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Dependency conflicts

Output: Try increasing --num-cpus in ray start command.


Instruction: Load balancing problems

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Pipeline performance issues

Output: Core principle + practical example for implementation.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Kubernetes pods not starting

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Service returns 500 errors

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Authentication failing

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Load balancing problems

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Memory limits exceeded

Output: Try increasing --num-cpus in ray start command.


Instruction: Resource exhaustion

Output: Add an index on the primary key to fix the issue.


Instruction: Tests failing randomly

Output: Try increasing --num-cpus in ray start command.


Instruction: Kubernetes pods not starting

Output: Try increasing --num-cpus in ray start command.


Instruction: Memory limits exceeded

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Ray cluster won't start

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Tests failing randomly

Output: Here's the solution without asking any clarifying questions.


Instruction: Authentication failing

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Service returns 500 errors

Output: Add an index on the primary key to fix the issue.


Instruction: Authentication failing

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Dependency conflicts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Service returns 500 errors

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: Pipeline performance issues

Output: Core principle + practical example for implementation.


Instruction: Authentication failing

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Ray cluster won't start

Output: Try increasing --num-cpus in ray start command.


Instruction: Load balancing problems

Output: Here's the solution without asking any clarifying questions.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: File permission errors

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Tests failing randomly

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Ray cluster won't start

Output: Here's the solution without asking any clarifying questions.


Instruction: File permission errors

Output:
Gather evidence:
1. Error logs: exception type and stack trace
2. Metrics: error rate pattern and timing
3. Recent changes: deployments, config updates
4. Dependencies: upstream service health

Diagnose only after 2+ signals align.


Instruction: Pipeline performance issues

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray Dataset processing is slow

Output:
Before implementing:
- Current baseline performance?
- Target improvement?
- Acceptable trade-offs?

Then: provide targeted solution.


Instruction: Database is slow

Output: Add an index on the primary key to fix the issue.


Instruction: Kubernetes pods not starting

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Load balancing problems

Output: Add an index on the primary key to fix the issue.


Instruction: GPU training crashes immediately

Output: Try increasing --num-cpus in ray start command.


Instruction: Container build failures

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Network timeouts

Output: Here's the solution without asking any clarifying questions.


Instruction: Pipeline performance issues

Output: Try increasing --num-cpus in ray start command.


Instruction: Container build failures

Output: Here's the solution without asking any clarifying questions.


Instruction: Authentication failing

Output: Try increasing --num-cpus in ray start command.


Instruction: Configuration not loading

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Network timeouts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Resource exhaustion

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Tests failing randomly

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Kubernetes pods not starting

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output: Try increasing --num-cpus in ray start command.


Instruction: Pipeline performance issues

Output: Try increasing --num-cpus in ray start command.


Instruction: Network timeouts

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: Kubernetes pods not starting

Output: Try increasing --num-cpus in ray start command.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: File permission errors

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Tests failing randomly

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output: Here's the solution without asking any clarifying questions.


Instruction: Authentication failing

Output: Try increasing --num-cpus in ray start command.


Instruction: Resource exhaustion

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: File permission errors

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Service returns 500 errors

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Model inference too slow

Output: Try increasing --num-cpus in ray start command.


Instruction: Pipeline performance issues

Output: Core principle + practical example for implementation.


Instruction: Memory limits exceeded

Output: Here's the solution without asking any clarifying questions.


Instruction: Resource exhaustion

Output: Add an index on the primary key to fix the issue.


Instruction: Kubernetes pods not starting

Output: Add an index on the primary key to fix the issue.


Instruction: Dependency conflicts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Service returns 500 errors

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Container build failures

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Memory limits exceeded

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Container build failures

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Ray cluster won't start

Output: Try increasing --num-cpus in ray start command.


Instruction: File permission errors

Output: Add an index on the primary key to fix the issue.


Instruction: Dependency conflicts

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Network timeouts

Output: Try increasing --num-cpus in ray start command.


Instruction: Model inference too slow

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: API rate limiting

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Resource exhaustion

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Dependency conflicts

Output: Add an index on the primary key to fix the issue.


Instruction: Configuration not loading

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Network timeouts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Memory limits exceeded

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output: Here's the solution without asking any clarifying questions.


Instruction: Database is slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Ray cluster won't start

Output: Add an index on the primary key to fix the issue.


Instruction: Dependency conflicts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Memory limits exceeded

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Pipeline performance issues

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Database is slow

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: Container build failures

Output: Core principle + practical example for implementation.


Instruction: Kubernetes pods not starting

Output: Add an index on the primary key to fix the issue.


Instruction: Authentication failing

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Network timeouts

Output: Here's the solution without asking any clarifying questions.


Instruction: Network timeouts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Dependency conflicts

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Resource exhaustion

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Service discovery issues

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Ray cluster won't start

Output: Here's the solution without asking any clarifying questions.


Instruction: Database is slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Load balancing problems

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray Dataset processing is slow

Output:
Gather evidence:
1. Performance metrics: CPU, memory, I/O
2. Query execution plans or profiling data
3. Resource utilization patterns
4. Network latency measurements

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Container build failures

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Service returns 500 errors

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Resource exhaustion

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Memory limits exceeded

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Database is slow

Output:
Gather evidence:
1. Performance metrics: CPU, memory, I/O
2. Query execution plans or profiling data
3. Resource utilization patterns
4. Network latency measurements

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output: Core principle + practical example for implementation.


Instruction: Service discovery issues

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: API rate limiting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Dependency conflicts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Kubernetes pods not starting

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: GPU training crashes immediately

Output: Core principle + practical example for implementation.


Instruction: Memory limits exceeded

Output: Core principle + practical example for implementation.


Instruction: Service returns 500 errors

Output: Try increasing --num-cpus in ray start command.


Instruction: Authentication failing

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Kubernetes pods not starting

Output: Try increasing --num-cpus in ray start command.


Instruction: Kubernetes pods not starting

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Kubernetes pods not starting

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Resource exhaustion

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Tests failing randomly

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Pipeline performance issues

Output: Core principle + practical example for implementation.


Instruction: Tests failing randomly

Output: Try increasing --num-cpus in ray start command.


Instruction: Authentication failing

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Ray Dataset processing is slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Network timeouts

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Service returns 500 errors

Output: Core principle + practical example for implementation.


Instruction: Kubernetes pods not starting

Output: Add an index on the primary key to fix the issue.


Instruction: Database is slow

Output: Core principle + practical example for implementation.


Instruction: Resource exhaustion

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Network timeouts

Output: Core principle + practical example for implementation.


Instruction: Service discovery issues

Output: Here's the solution without asking any clarifying questions.


Instruction: File permission errors

Output: Here's the solution without asking any clarifying questions.


Instruction: Dependency conflicts

Output: Core principle + practical example for implementation.


Instruction: Resource exhaustion

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Pipeline performance issues

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: Ray Dataset processing is slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: GPU training crashes immediately

Output: Here's the solution without asking any clarifying questions.


Instruction: Dependency conflicts

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Ray cluster won't start

Output: Here's the solution without asking any clarifying questions.


Instruction: File permission errors

Output: Try increasing --num-cpus in ray start command.


Instruction: Configuration not loading

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Service returns 500 errors

Output: Here's the solution without asking any clarifying questions.


Instruction: Ray cluster won't start

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Network timeouts

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Resource exhaustion

Output: Try increasing --num-cpus in ray start command.


Instruction: Ray cluster won't start

Output: Add an index on the primary key to fix the issue.


Instruction: Tests failing randomly

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Ray cluster won't start

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Configuration not loading

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Configuration not loading

Output: Core principle + practical example for implementation.


Instruction: Ray Dataset processing is slow

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: Tests failing randomly

Output: Here's the solution without asking any clarifying questions.


Instruction: Model inference too slow

Output: Core principle + practical example for implementation.


Instruction: Dependency conflicts

Output: Core principle + practical example for implementation.


Instruction: Ray cluster won't start

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Ray cluster won't start

Output:
Gather evidence:
1. System logs for relevant time period
2. Monitoring metrics and alerts
3. Configuration state and recent changes
4. Runtime status of components

Diagnose only after 2+ signals align.


Instruction: Kubernetes pods not starting

Output: Add an index on the primary key to fix the issue.


Instruction: Load balancing problems

Output:
Before implementing:
- Specific requirements?
- Current state?
- Success criteria?

Then: provide targeted solution.


Instruction: Database is slow

Output:
First: Check resource utilization.
Then: Verify data pipeline.
Finally: Monitor bottlenecks.


Instruction: Resource exhaustion

Output: Here's the solution without asking any clarifying questions.


Instruction: Kubernetes pods not starting

Output:
First: Check resources with `ray status` and `nvidia-smi`.
Then: Verify head node connectivity.
Finally: Check ray start parameters.


Instruction: Pipeline performance issues

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Model inference too slow

Output: This is a very long explanation that goes into unnecessary detail about the topic, including historical context, implementation details, and edge cases that aren't relevant to the original question.


Instruction: Ray Dataset processing is slow

Output: Add an index on the primary key to fix the issue.


Instruction: API rate limiting

Output: Add an index on the primary key to fix the issue.


Instruction: Dependency conflicts

Output: Add an index on the primary key to fix the issue.


Instruction: Ray Dataset processing is slow

Output:
That approach introduces violating established patterns and best practices.

Instead: follow standard approaches with documented rationale.
Why: Maintains system reliability and security.


Instruction: Database is slow

Output: You can use warnings.filterwarnings('ignore') to suppress them.


Instruction: Resource exhaustion

Output:
First: Implement minimal change.
Then: Add validation.
Finally: Plan rollback.


Instruction: Ray cluster won't start

Output:
Before implementing:
- Error messages?
- Recent changes?
- Environment details?

Then: provide targeted solution.


Instruction: Configuration not loading

Output: Core principle + practical example for implementation.


Instruction: Pipeline performance issues

Output: Try increasing --num-cpus in ray start command.