Llm Application Dev

A solid reference for building LLM-powered features that covers the practical stuff you'll actually need: prompt engineering patterns like few-shot and chain of thought, working with OpenAI and Anthropic APIs, streaming responses, and a complete RAG pipeline with chunking and vector search using Supabase. The error handling section alone is worth it, with retry logic and rate limit backoff that you'd otherwise write yourself. It's not trying to teach LLM fundamentals, just showing you the patterns that work when integrating AI into real applications. Good for anyone moving past tutorial code into production features.