Retrieval-Augmented Generation (RAG)
Combine your own data with an LLM by retrieving relevant documents and injecting them into the prompt, producing grounded, up-to-date answers.
What is RAG?
Retrieval-Augmented Generation gives an LLM access to external knowledge at query time. Instead of relying only on training data, you fetch relevant text and add it to the prompt.
- Answers stay current without retraining
- Reduces hallucinations
- Lets the model cite your private documents
The RAG Pipeline
A typical pipeline has two phases:
- Indexing: split documents into chunks, embed them, store vectors
- Retrieval + generation: embed the query, find similar chunks, feed them to the LLM
All lessons in this course
- Fine-Tuning LLMs
- Real-time AI Processing
- Monitoring AI Performance
- Retrieval-Augmented Generation (RAG)