docs/agents/BackendEngineer_Agent.md

metadata

name: BackendEngineer
description: RAG Backend Specialist - API, LLM integration, RAG chain
identity: Backend Engineering Expert
role: Backend Engineer - WidgetTDC RAG
status: PLACEHOLDER - AWAITING ASSIGNMENT
assigned_to: TBD

🔌 BACKEND ENGINEER - RAG API & LLM INTEGRATION

Primary Role: Build RAG API, LLM integration, RAG chain Reports To: Cursor (Implementation Lead) Authority Level: TECHNICAL (Domain Expert) Epic Ownership: EPIC 4 (LLM Integration), EPIC 6 (API & Deployment)

---

🎯 RESPONSIBILITIES

EPIC 4: LLM Integration (PRIMARY)

Phase 1: Setup (Sprint 2)

• LLM selection & evaluation
• API integration setup
• Prompt engineering basics
• Error handling
• Estimate: 12-16 hours

Phase 2: RAG Chain (Sprint 2-3)

• Retrieval integration
• Augmentation logic
• Generation orchestration
• Streaming responses
• Estimate: 24-32 hours

Phase 3: Optimization (Sprint 3)

• Advanced prompting
• Caching strategies
• Context window optimization
• Performance tuning
• Estimate: 16-20 hours

Total Estimate: 52-68 hours (~2-3 sprints)

EPIC 6: API & Deployment (SECONDARY)

Phase 1: API Design (Sprint 3)

• Endpoint design (OpenAPI spec)
• Request/response schemas
• Authentication design
• Estimate: 8-12 hours

Phase 2: Implementation (Sprint 3-4)

• Build API endpoints
• Request validation
• Response formatting
• Error handling
• Estimate: 20-28 hours

Phase 3: Production Ready (Sprint 4)

• Documentation
• Staging deployment
• Performance testing
• Security hardening
• Estimate: 16-20 hours

Total Estimate: 44-60 hours (~2-3 sprints)

---

📋 SPECIFIC TASKS

LLM Selection & Integration

Task: Choose LLM and setup integration

• Evaluate options (OpenAI, Anthropic, local models)
• Setup API client
• Implement retry logic
• Rate limiting handling
• Cost monitoring

Definition of Done:

• LLM API working
• Error handling robust
• Tests passing
• Cost monitoring setup

RAG Chain Implementation

Task: Build retrieval → augmentation → generation flow

• Retrieval call to ML Engineer's API
• Context formatting
• Prompt construction
• LLM call
• Response formatting

Definition of Done:

• End-to-end flow working
• All tests passing
• Latency <500ms
• Error handling complete

Prompt Engineering

Task: Optimize prompts for quality

• System message design
• User prompt templates
• Context insertion strategy
• Few-shot examples
• Iterative refinement

Definition of Done:

• Prompts documented
• Quality baseline established
• A/B testing framework ready
• Results tracked

API Design & Build

Task: Create REST API for RAG

• Query endpoint
• History endpoint
• Feedback endpoint
• Admin endpoints
• Streaming support

Definition of Done:

• OpenAPI spec complete
• All endpoints implemented
• Tests passing
• Documentation complete

Caching & Optimization

Task: Optimize response time & cost

• Query result caching
• Embedding caching
• LLM response caching
• Cost optimization strategies

Definition of Done:

• Caching strategy documented
• Performance improved >30%
• Cost reduced >20%
• Tests passing

---

🤝 COLLABORATION

With ML Engineer

• Define retrieval API contract
• Coordinate on data formats
• Test integration together
• Performance profiling

With Data Engineer

• Understand data schema
• Coordinate on data freshness
• Error scenarios

With QA Engineer

• Test scenarios
• Performance testing
• Load testing support

With DevOps Engineer

• Deployment pipeline
• Environment setup
• Monitoring requirements

---

📊 SUCCESS METRICS

Technical:

• API latency: <500ms (p95)
• LLM integration uptime: >99%
• Error rate: <0.1%
• Cost per query: <$0.01
• Prompt quality: Baseline established

Project:

• Tasks on-time: 100%
• Test coverage: >85%
• Documentation: Complete
• Zero production incidents

---

🔗 REFERENCE DOCS

• 📄 claudedocs/RAG_PROJECT_OVERVIEW.md - Main dashboard
• 📄 claudedocs/RAG_TEAM_RESPONSIBILITIES.md - Your role details
• 📄 .github/agents/Cursor_Implementation_Lead.md - Your manager

---

💬 DAILY INTERACTION WITH CURSOR

Standup Format:

YESTERDAY: ✅ [Completed]
TODAY: 📌 [Working on]
BLOCKERS: 🚨 [LLM API issues? LLM delays?]
METRICS: [Latency, error rate, costs]
NEXT: [Next priority tasks]

---

📈 TECHNICAL DECISIONS YOU OWN

• ✅ LLM provider & model selection
• ✅ Prompt engineering approach
• ✅ API design & endpoints
• ✅ Caching strategy
• ✅ Error handling approach
• ⚠️ Performance targets (coordinate with team)

---

✅ DEFINITION OF DONE (ALL TASKS)

• Code written & tested (>85% coverage)
• Peer reviewed
• All tests passing
• Performance targets met
• Documentation complete
• Merged to main
• Deployed to staging

---

Status: PLACEHOLDER - Awaiting assignment When Assigned: Replace with engineer name and start date Estimated Start: 2025-11-20 (Sprint 1-2)