RAG pipeline explained

RAG Pipeline Explained

A RAG pipeline gives an AI app the right context before it answers. Instead of relying on model memory, the system retrieves relevant documents and uses them to generate grounded responses.

RAG roadmap topic Vector databases Evaluating RAG AI Agents Explained

What it is

RAG means retrieval-augmented generation. It combines search with an LLM so answers can use private, fresh, or domain-specific information.

Why it matters

Most useful AI products need facts outside the model. RAG reduces hallucination, enables citations, and lets teams update knowledge without retraining a model.

How it works

Load documents, split them into chunks, embed the chunks, store vectors, retrieve top matches for a query, place those matches into a prompt, then generate and evaluate the answer.

Common mistakes

  • Chunking documents blindly by fixed character count.
  • Only checking final answers instead of retrieved chunks.
  • Ignoring metadata filters for permissions and categories.

Best practices

  • Inspect retrieved chunks for every failure.
  • Track retrieval hit rate separately from answer quality.
  • Start simple before adding reranking or complex agents.

Practical Workflow

query -> retrieve relevant chunks -> build prompt with sources -> generate answer -> evaluate citations

FAQ

Is RAG better than fine-tuning?

Use RAG when the model needs external knowledge. Use fine-tuning when you need behavior, style, or task adaptation.

Do I need a vector database for RAG?

For production, usually yes. For small demos, local vector stores or simple semantic search can be enough.

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