Retrieval-Augmented Generation (RAG) Explained

Retrieval-augmented generation retrieves relevant evidence before an LLM answers. A production RAG system must control indexing, ranking, context construction, citations, and evaluation—not just connect a vector database to a prompt.

You will be able to: Build and evaluate a retrieval pipeline that can show which evidence supports each answer.

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Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Build a complete RAG pipeline
Diagnose retrieval vs generation failures
Architect secure, observable knowledge systems

Helpful before starting

LLM prompting and embeddings
Information retrieval and similarity metrics
Basic data pipelines and APIs

Start here

RAG, in plain language

Retrieval Augmented Generation for grounding AI. RAG grounds generation in external knowledge, but its quality depends on an entire retrieval and evidence pipeline rather than the vector database alone.

For a small example, one claim cites allowed doc-1, one has no citation, and one cites allowed doc-2 plus unknown doc-9. Resolve citation handles against the selected evidence, count two of three claims as covered, then check semantic support separately. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Ingestion, chunking, embeddings, indexing, retrieval, context construction, and generation.
Lexical vs dense retrieval and cosine, dot-product, or Euclidean similarity.
Grounding, citations, context windows, and basic evaluation.

Use it well

When RAG helps—and where it breaks

Product documentation changes every week and some passages are permissioned. Retrieve candidates, rerank, filter permissions before Top-K selection, generate claims, then report retrieval recall separately from citation coverage and semantic support. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Tuning prompts to hide poor retrieval. Better approach: Measure recall and ranking before changing generation.
Applying permissions after retrieval. Better approach: Filter before or during retrieval to prevent data exposure.
Assuming every document should use one chunk size. Better approach: Tune chunking by document structure and question type.

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