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intermediate
RAG (Retrieval-Augmented Generation)

Building a RAG System with LangChain

Understand the complete RAG pipeline: loading, chunking, embeddings, indexing, retrieval, prompting, citations, and evals

35 min read· RAG· LangChain· Pipeline· Document Processing

Building a RAG System with LangChain

RAG connects a model to external knowledge.

The model should answer from retrieved evidence, not from memory alone.

Pipeline

text
load docs -> split chunks -> embed chunks -> store index -> retrieve -> answer with citations

Key components

ComponentJob
loadergets text from files or systems
splittercreates chunks
embedding modelturns chunks into vectors
vector storesearches vectors
retrieverreturns candidate evidence
prompttells model how to use evidence
evaluatorchecks groundedness

Answer prompt rule

Use rules like:

text
Answer only from provided context.
If the answer is missing, say so.
Include citations.

Build order

  1. Index a tiny corpus.
  2. Retrieve top chunks.
  3. Add citations.
  4. Add no-answer behavior.
  5. Add evals.
  6. Add monitoring.

Knowledge check

Q1: Why does RAG need citations?

So users can verify the answer.

Q2: What should happen when retrieval fails?

The system should say it lacks evidence.