What is RAG?
Retrieval-Augmented Generation combines vector search for relevant context with LLM processing to generate accurate, grounded responses.
Core Components
1. Data Ingestion Pipeline
Document parsing (PDF, Markdown, HTML, etc.)Chunking strategy (semantic vs fixed-size)Embedding generation with domain-specific modelsVector database storage and indexing2. Retrieval Layer
Semantic search using vector similarityHybrid search combining vector + keywordReranking for relevance optimizationContext selection and compression3. Generation Layer
Prompt engineering with retrieved contextLLM integration with streaming supportResponse formatting and validationQuality control and fact-checkingProduction Considerations
Monitor retrieval quality with relevance metricsImplement caching for common queriesOptimize costs with selective model usageHandle edge cases with fallback strategiesVersion control for prompts and configurations