What is RAG?

RAG vs Fine-tuning — know when to use each

A Fortune 500 company spent $2M on an internal knowledge base. Engineers still Slacked each other instead of searching it. The search was keyword-based — it couldn't understand 'how do I handle auth timeouts?' because the docs said 'authentication retry logic.' RAG fixed this in 3 weeks.
— Level 1 · Production RAG Pipeline

+100 XP5 min1 / 10

What is Retrieval-Augmented Generation?

Large Language Models are powerful, but they have a fundamental flaw: hallucination. They generate plausible-sounding answers that are factually wrong because their knowledge is frozen at training time.

RAG solves this by adding a retrieval step before generation. Instead of relying solely on the model's memory, RAG fetches relevant documents from an external knowledge base and feeds them to the LLM as context. The model then generates answers grounded in real, up-to-date data — with citations pointing back to the source.

The key insight: RAG separates what the model knows (general language ability) from what it can access (your specific documents). You update the knowledge base, not the model. This means updates happen in minutes, not days of retraining.

Enterprise Skills Bridge: Think of the vector store as an index on a database. Just as a B-tree index on a SQL column makes lookups O(log n) instead of O(n), a vector index on document embeddings makes semantic search fast. Same concept — different data type.

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