ai-llm-patterns

name	ai-llm-patterns
description	AI/LLM integration patterns: Anthropic SDK streaming with TypeScript, RAG architecture (chunking, embedding, vector search with pgvector), tool use / function calling, model selection guide (Haiku 4.5 vs Sonnet 5 vs Opus 4.8), prompt caching, structured output with Zod, token cost management. Use when building AI features, RAG pipelines, AI agents.
allowed-tools	Read, Grep, Glob

AI/LLM Integration Patterns

When to Use This Skill

• Building AI features with Claude / Anthropic SDK
• Implementing RAG (Retrieval-Augmented Generation)
• Using tool use / function calling patterns
• Choosing the right Claude model for the task
• Managing token costs and prompt caching

Core Principles

1. Model selection matters for cost — Haiku is 10× cheaper than Sonnet; Sonnet is 5× cheaper than Opus; use the smallest model that can do the task
2. Streaming for user experience — start showing text immediately; never make users wait for full response
3. Tool use for deterministic operations — LLM decides what to do; your code does it safely; never trust LLM output directly for DB writes
4. Prompt caching for repeated context — system prompts, documents, RAG context: cache if >1024 tokens
5. RAG chunk size matters — too small (50 tokens) = no context; too large (2000 tokens) = irrelevant context dilutes relevance

References available

• references/prompt-engineering.md — streaming with SSE, model selection guide (Haiku/Sonnet/Opus), prompt caching, structured output with Zod
• references/rag-architecture.md — chunking strategy, embedding with text-embedding-3-small, pgvector cosine search, retrieval pipeline
• references/llm-ops.md — tool use / agent loop pattern, cost tracking, token budgeting, multi-model routing, error handling