Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.

LLM Application Development Prompt Engineering Structured Prompts Few Shot Examples Chain of Thought API Integration OpenAI Pattern Anthropic Pattern Streaming Responses RAG (Retrieval Augmented Generation) Basic RAG Pipeline Document Chunking Embedding Storage Error Handling Best Practices Token Management : Track usage and set limits Caching : Cache embeddings and common queries Evaluation : Test prompts with diverse inputs Guardrails : Validate outputs before using Logging : Log prompts and responses for debugging Cost Control : Use cheaper models for simple tasks Latency : Stream responses for better UX Privacy : Don't send PII to external APIs

llm-application-dev