LLM & RAG Application Engineering Skill

This skill defines industry standards for developing, evaluating, and operating LLM, RAG, and AI Agent features cleanly.

Core Architectural Pillars

1. Evaluation & Benchmarking (Evals)

• Deterministic Evals: Assert output schema validity (Zod/Pydantic validation).
• Model-Graded Evals: Evaluate accuracy, grounding (faithfulness), hallucination rate, and context relevance using LLM-as-a-Judge.
• RAG Triad:
  • Context Relevance (Retrieval quality)
  • Groundedness (LLM stays within retrieved context)
  • Answer Relevance (Output addresses user prompt directly)

2. Token & Cost Budget Guardrails

• Max Token Limits: Hard limit max response tokens per prompt.
• Circuit Breakers: Halt downstream requests if daily/hourly token expenditure exceeds set threshold.
• Semantic Caching: Store prompt/embedding responses in Redis/pgvector to eliminate redundant LLM calls.

3. AI Security & Safety

• Prompt Injection Defense: Sanitize user inputs; separate system instructions from untrusted user content.
• PII Scrubbing: Redact secrets, emails, SSNs, and credit card numbers before sending payloads to LLM APIs.
• OWASP Top 10 for LLMs: Guard against Insecure Output Handling, Excessive Agency, and Data Poisoning.

Checklist for Feature Slices with has_llm: true

• Schema validation for structured output (JSON mode / Tool Call response parsing)
• Fallback model strategy (e.g., fallback from primary model to secondary model on rate limit)
• OpenTelemetry LLM tracing (LangSmith / Helicone / Phoenix / OTel instrumentation)
• Token usage logging and latency tracking
• Eval dataset created with at least 10 gold-standard ground truth examples