Building RAG Applications
Combine LLMs with your own data using retrieval-augmented generation
RAG (retrieval-augmented generation) lets models answer questions using your private data. Instead of trusting the model’s memory, you retrieve relevant documents and inject them into the prompt. This guide covers the core flow, chunking strategies, and how to evaluate quality without guessing.
RAG Flow
Typical flow: 1. User query 2. Embed query 3. Retrieve relevant documents 4. Inject context 5. Generate response
Chunking Strategies
Good chunking: • Improves retrieval accuracy • Reduces hallucinations • Balances context size
Evaluating RAG Systems
Measure: • Retrieval relevance • Answer accuracy • Latency • Cost per query
Common Failure Modes
Watch out for: • bad chunking (too large/too small) • stale documents • missing citations • retrieval returning irrelevant text • prompt too long → truncation