RAG fixes a basic problem: language models hallucinate when asked about things they weren't trained on. The fix is to retrieve relevant documents from your own data, paste them into the prompt as context, and ask the model to answer based only on those documents.
A typical RAG pipeline has four steps. (1) Ingest: chunk your documents and embed each chunk into a vector. (2) Index: store those vectors in a vector database (Pinecone, Weaviate, pgvector). (3) Retrieve: at query time, embed the user's question and pull the top-K most similar chunks. (4) Generate: feed the chunks plus the question to the model and ask it to answer with citations.
The hard parts are usually retrieval quality (your top-3 results need to actually contain the answer) and chunking strategy. Most teams over-rely on vector search and ignore basic things like keyword search, metadata filters, and re-ranking — all of which dramatically improve results.
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