AI RAG Tutorial: Vector Stores, Embeddings, Chunking

TL;DRRAG pipelines chunk documents, embed them, store vectors, and retrieve by similarity.

The mechanics: split documents into chunks, embed each into a vector, store them in a vector database. At query time, embed the question and retrieve the nearest chunks by vector similarity, then pass them to the LLM. Chunk size, overlap, and embedding quality make or break results — too-big chunks dilute relevance, too-small lose context. RAG is mostly a retrieval-quality problem.

Key points

Chunk → embed → store in a vector DB
Query: embed question → nearest chunks
Chunk size/overlap heavily affect quality
RAG success ≈ retrieval quality

Common mistakes

Bad chunking (too big/small)
Poor embedding model for the domain
Retrieving too many low-relevance chunks

Try it: Explain how chunk size affects retrieval relevance.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>docs → chunks → embeddings → vector DB
query → embed → nearest chunks → LLM
chunking quality decides the answer</pre></body></html>

Open the interactive lesson →

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