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Implementing RAG with Vector Databases

A practical guide to building retrieval-augmented generation pipelines: chunking, embeddings, vector search, and grounding LLM answers in your own data.

AIBackend· Tuspa Engineering· 2026-05-12

Why RAG?

Large language models are powerful but frozen in time and unaware of your private data. Retrieval-Augmented Generation (RAG) fixes both problems by retrieving relevant documents at question time and letting the model answer grounded in them.

A minimal RAG pipeline has four stages:

  1. Ingest — split source documents into chunks
  2. Embed — convert each chunk into a vector
  3. Retrieve — find the chunks closest to the user query
  4. Generate — pass query + chunks to the LLM

Chunking strategy

  • Keep chunks semantically coherent
  • Overlap chunks by 10–15%
  • Store the source URL with every chunk for citations

Always show your sources. Grounded answers with citations earn user trust and make hallucinations visible.

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