What Is RAG? Retrieval-Augmented Generation Explained
AI models are powerful, but they do not know your company's documents, your product specs, or last week's meeting notes. RAG — Retrieval-Augmented Generation — is the pattern that solves this by feeding relevant information to AI at query time, without retraining the model. This path explains how RAG works end to end: from turning text into embeddings, to searching a vector database, to assembling the perfect context for an accurate, grounded response.
What You'll Learn
- 1Why AI has a knowledge problem and how RAG solves it without fine-tuning
- 2Embeddings: how AI converts meaning into mathematical vectors you can search
- 3Vector databases and similarity search — the engine behind RAG retrieval
- 4The complete RAG pattern: retrieve, augment, generate
- 5Chunking strategies that determine the quality of your retrieval results
- 6Context management techniques that keep retrieved information useful and focused
Curated Lessons (7)
Free, interactive lessons you can complete on your phone in 5-10 minutes each.
The Knowledge Problem
RAG — Giving AI Your Knowledge
Embeddings — Turning Meaning into Math
RAG — Giving AI Your Knowledge
Vector Search
RAG — Giving AI Your Knowledge
The RAG Pattern
RAG — Giving AI Your Knowledge
Chunking Strategies
RAG — Giving AI Your Knowledge
What Is Context?
Context — The Most Important Concept
Context Management
Context — The Most Important Concept
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