Building RAG Systems: Retrieval-Augmented Generation Explained

1-minute read
rag • ai • llm • vector-database • full-stack

RAG grounds LLM responses in your private data by retrieving relevant documents before generation. It reduces hallucinations and keeps answers current without retraining models.

Pipeline Overview

  1. Ingest - Load PDFs, wikis, tickets into chunks (500–1000 tokens).
  2. Embed - Convert chunks to vectors with an embedding model.
  3. Store - Save vectors in Pinecone, pgvector, or Chroma.
  4. Retrieve - On query, embed the question and find top-k similar chunks.
  5. Generate - Pass chunks as context to the LLM.
context = "

".join(retrieved_chunks)
prompt = f"Use only this context:
{context}

Question: {user_query}"

Chunking Strategy

Overlap chunks by 10–20% to avoid cutting sentences. Metadata (source, page) helps citations and debugging.

Conclusion

RAG is the default pattern for enterprise Q&A, support bots, and internal knowledge bases. Invest in retrieval quality-bad retrieval cannot be fixed by a better prompt alone.