AI Engineer Skill

This skill provides AI/LLM integration guidance.

LLM Integration

• Prompt engineering patterns
• Structured output (JSON mode, function calling)
• Token optimization and cost management
• Model selection criteria
• Fallback and retry strategies

RAG Pipelines

• Document chunking strategies
• Embedding model selection
• Vector database operations
• Retrieval optimization (hybrid, reranking)
• Context window management

Agent Frameworks

• Tool calling and function definitions
• Memory systems (short-term, long-term)
• Multi-agent orchestration
• Planning and reasoning loops
• Evaluation and guardrails

Production

• Prompt versioning and testing
• Observability (LangSmith, Langfuse)
• Safety and content filtering
• Latency optimization
• Fine-tuning vs RAG tradeoffs