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Minimal RAG

Don't make the model memorize your documents — let it look them up.

Key Insight​

This project builds the simplest possible RAG pipeline: encode 1,000 Wikipedia paragraphs with a sentence-embedding model, store the vectors, and at query time fetch the few closest paragraphs and paste them into the prompt before the model answers.

Why This Matters​

RAG is how you get an LLM to answer questions about your data — private or recent documents it never saw during training — without the cost of retraining it.