What Is RAG?
Retrieval-Augmented Generation gives LLMs access to your data. Instead of relying on training data (which may be outdated or wrong), RAG retrieves relevant documents and includes them in the prompt. The LLM generates answers grounded in your actual sources — reducing hallucinations and providing up-to-date information.
RAG Architecture
The pipeline: Ingest → chunk documents → generate embeddings → store in vector DB. Query → embed user question → search vector DB → retrieve top-k chunks → send chunks + question to LLM → return grounded response. Each step affects quality — especially chunking and retrieval.
Embeddings and Vector Databases
Embeddings convert text into high-dimensional vectors. Similar texts have similar vectors. Use OpenAI's ada-002, Cohere's embed, or open-source models like BGE. Store vectors in Pinecone (managed), Weaviate (open-source), pgvector (PostgreSQL extension), or Chroma (lightweight). For small datasets, pgvector is the simplest choice.
Chunking Strategies
Too small: chunks lack context. Too large: irrelevant noise dilutes useful information. Start with 500-1000 tokens with 100-200 token overlap. Use semantic chunking (split by sections/paragraphs) over fixed-size chunking. Include metadata (source, date, section) with each chunk for filtering and attribution.
Retrieval Optimization
Hybrid search combines vector similarity with keyword matching (BM25). Re-ranking with cross-encoder models improves result quality. Use metadata filters to narrow results (by date, category, source). Query expansion rewrites the user's question for better retrieval. These techniques can improve answer quality by 30-50%.
Frameworks and Tools
LangChain: full-featured, Python/JS, lots of integrations. LlamaIndex: data-focused, excellent for document ingestion. Haystack: production-grade, modular pipelines. For simple RAG, direct API calls to embeddings + vector DB + LLM may be cleaner than a framework.
Conclusion
RAG is the most practical way to give AI access to your data in 2026. Start with a simple pipeline: chunk your docs, embed with ada-002, store in pgvector, retrieve top-5, and pass to GPT. Iterate on chunking and retrieval as you learn what works for your data.