developer-roadmap/roadmaps/forward-deployed-engineer/content/rags@7YOfKFFLI78GabP760sy8.md

RAGs

Retrieval-Augmented Generation (RAG) is a pattern where relevant documents or data are fetched from a knowledge source and injected into a model's context before generating a response. This gives the model access to customer-specific or up-to-date information without requiring fine-tuning. RAG is one of the most common patterns FDEs implement when building AI features on top of a customer's proprietary data.

Visit the following resources to learn more:

• @article@What is RAG?
• @article@RAG Explained: Understanding Embeddings, Similarity, and Retrieval
• @video@What is Retrieval-Augmented Generation? IBM