RAG (Retrieval-Augmented Generation)
Grounding model output in real, retrieved data instead of letting it answer from imagination.
Related articles
Blog posts where RAG (Retrieval-Augmented Generation) shows up in practice.
RAG (Retrieval-Augmented Generation)
RAG keeps a model honest by having it answer only from data retrieved for the current request, rather than from what it remembers or imagines. The core pattern is three steps: understand the request, retrieve relevant real data (usually via vector/semantic search), then generate a response constrained to what was retrieved.
📚 Guides
- 🔍 Keyword search vs. vector search, why semantic retrieval beats exact-match search for conversational input