Interactive Learning Lab

Retrieval-Augmented Generation

Don't just read about RAG — experience it. Five hands-on labs that show you exactly how it works, step by step, with real API calls.

GPT-4o-mini text-embedding-3-small Real API calls Nothing hidden
Follow the labs in order for the full story
01
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The ProblemProblem #1
The Hallucination Problem
Ask GPT-4o-mini about NovaCorp's private HR policy and watch it confidently make up wrong answers.
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02
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The ProblemProblem #2
Keyword vs Semantic Search
Search the same corpus two ways. See why exact keyword matching fails where semantic search succeeds.
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03
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The SolutionThe Foundation
What Are Embeddings?
Type text and watch it become a 1536-dimensional vector. Compare sentences and see their similarity score.
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04
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The SolutionBuilding the Index
The Vector Database
Upload a PDF and step through the full pipeline: extract → chunk → embed → index → search.
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05
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The PayoffEverything Together
The Full RAG Pipeline
Watch your question embed, retrieve chunks, build a prompt, and stream a grounded answer — all in real time.
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The Story Arc
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Lab 1–2
Two problems with LLMs & keyword search
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Lab 3
Embeddings: the mathematical foundation
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Lab 4
Build the vector index step by step
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Lab 5
Full RAG — all pieces working together