Retrieval-Augmented Generation (RAG) Basics
Learn how RAG grounds LLM responses in your own documents by retrieving relevant context at query time and feeding it into the prompt.
The Knowledge Gap
An LLM only knows what it was trained on. It cannot answer questions about your private docs or recent events. RAG (Retrieval-Augmented Generation) closes this gap by fetching relevant text and putting it in the prompt.
The Core Idea
Instead of fine-tuning the model on your data, you retrieve the most relevant snippets at query time and let the model answer using them as context. Cheaper, faster to update, and easy to cite.
All lessons in this course
- LLM API Interaction (OpenAI, Anthropic)
- LangChain & LlamaIndex Basics
- Prompt Management & Versioning
- Retrieval-Augmented Generation (RAG) Basics