RAG from First Principles
Build a Retrieval-Augmented Generation system from first principles using Python and Scikit-Learn. No vector databases, just the mechanics.
LLMs are frozen in time. They know nothing about your company's latest policies, your private documents, or today's news.
To fix this, we'll use RAG (Retrieval-Augmented Generation)1.
Instead of retraining the model (expensive and slow) or stuffing entire documents into the prompt (expensive and limited by context windows), RAG creates a system that:
- Retrieves only the relevant chunks from your data
- Augments the prompt by injecting those chunks as context
- Generates an answer grounded in your specific information
In this tutorial, we'll build RAG from scratch. Just Python and math to understand that RAG is just search plus prompt injection.
What You'll Build
- A simple text chunking system for breaking documents into searchable pieces
- A retrieval engine using TF-IDF and Cosine Similarity
- A context-aware prompt template that injects retrieved information
- A working system that shows how context reduces hallucinations
How RAG Works
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What you get
6 modules and 42 hands-on lessons with runnable code, failure diagnosis, and measured comparisons
2 capstone repositories you build, test, evaluate, and keep as portfolio proof
Videos, source code, complete projects and architecture diagrams
Recordings of live sessions
Discord community access
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