# AI LLM

> Use for LLM application development, RAG systems, prompt engineering, AI agent orchestration, vector databases, embedding strategies, similarity search, LangChain architecture, LLM evaluation, and AI context management.

- Skill: `henryhawke/ai-llm` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add henryhawke/ai-llm`
- Raw SKILL.md: https://api.skillmd.com/api/skills/henryhawke/ai-llm/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: henryhawke (https://skillmd.com/u/henryhawke)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/henryhawke/ai-llm

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# AI & LLM Development

## When to use
- Build LLM-powered applications
- Implement RAG (Retrieval-Augmented Generation)
- Design and optimize prompts
- Build AI agents and tool-using systems
- Set up vector databases and embeddings
- Evaluate LLM outputs

## RAG architecture
- Document chunking strategies
- Embedding model selection
- Vector database setup (Pinecone, Weaviate, pgvector)
- Hybrid search (vector + keyword)
- Re-ranking and relevance tuning

## Prompt engineering
- Chain-of-thought reasoning
- Few-shot and zero-shot patterns
- Constitutional AI techniques
- System prompt design
- Output formatting and validation

## Agent patterns
- Tool-using agents
- Multi-agent orchestration
- Memory and context management
- LangChain/LangGraph architecture
- Evaluation frameworks