Dashboard
LLM Evaluation, Regression Testing & AI Quality Monitoring Platform
Model Leaderboard
Aggregated benchmarks graded by LLM-as-a-Judge.
| Rank | Model | Accuracy | Avg Latency |
|---|---|---|---|
| 1 | Claude 3.5 Sonnet | 4.50 / 5.0 | 850 ms |
| 2 | GPT-4o | 4.50 / 5.0 | 1240 ms |
| 3 | Gemini 1.5 Pro | 4.20 / 5.0 | 980 ms |
| 4 | Gemini 1.5 Flash | 3.80 / 5.0 | 420 ms |
Platform Diagnostics
LLM Cost-to-Quality Efficiency Frontier
Quality vs. Cost Scale (Bubble Size = Latency)
Recent Evaluation Runs
| Run ID | Dataset ID | Prompt ID | Status | Accuracy | Hallucination Rate | Cost | Timestamp |
|---|---|---|---|---|---|---|---|
| Loading evaluation runs... | |||||||
Dataset Management
Manage evaluation test suites, categories, and test cases.
Dataset Details
No description provided.
| ID | Category | Question | Expected Output (Ground Truth) | Actions |
|---|---|---|---|---|
| No test cases in this dataset. | ||||
Delete Test Case
Upload JSON Test Cases Array
[
{
"question": "What is diabetes?",
"ground_truth": "Diabetes is a chronic disease where the body cannot regulate glucose...",
"category": "factual",
"meta_data": {"difficulty": "easy"}
}
]Append Single Test Case
Prompt Arena & Benchmark
Compare prompt templates and rank versions by accuracy, latency, and cost.
Prompt Arena Comparison Playground
Evaluation Hub & Pipeline
Schedule evaluation runs, monitor real-time execution, and inspect historical logs.
Run Configurations
Execution Status
Current Pipeline Run Status: PENDING
Run Metadata — Status: COMPLETED
Created At: N/A
Granular Test Results
| TestCase ID | Model | Question | Ground Truth | Model Output | Scores | Judge Explanation | Latency | Cost |
|---|---|---|---|---|---|---|---|---|
| No results found for this run. | ||||||||
AI Agent Console
Leverage intelligent agent reasoning to debug failures and generate adversarial test cases.
Failure Debugger & Optimizer Agent
When prompts change or database schema updates occur, evaluation scores can degrade. Select a run to analyze failures.
Adversarial Dataset Generator
Generate ambiguous variations, info-traps, and injection payloads to stresstest model robustness.
Adversarial Suite Created Successfully
Saved as new Dataset: Name (ID)
Generated Adversarial Test Cases
| Attack Type | Question | Expected Output (Ground Truth) | Original Question Reference |
|---|---|---|---|
| No adversarial cases generated. | |||
Cost Optimizer & Analytics
Analyze model spending, efficiency ratios, and get LLM routing recommendations.
Cost per Query by Model
Accuracy-to-Cost Efficiency Ratio
Calculated as (Accuracy Score / Cost in Millidollars). Higher means more value per dollar.