Implementing RAG: Retrieval-Augmented Generation in Practice

RAG combines the power of large language models with your specific data. Instead of relying on the model training data, RAG retrieves relevant documents and uses them as context.

The pipeline: ingest documents, chunk text, generate embeddings, store in vector database, retrieve relevant chunks, augment prompt, generate response.

Choose your vector database based on scale: pgvector for small datasets, Pinecone or Weaviate for larger ones. Chunk size and overlap matter — experiment with 500-1000 token chunks with 100 token overlap.