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Read the privacy noticeUsed in: alex-os-project, multi-agent-rag
Used to ground language-model responses in CMS content, documents and related knowledge.
Used to ground language-model responses in CMS content, documents and related knowledge.
Grounding Alex OS's answers in indexed CMS content instead of the model's own knowledge.
Grounding Alex OS's answers in indexed CMS content instead of the model's own knowledge.
RAG is how Alex keeps language-model answers grounded in actual content rather than model guesswork — actively used in Alex OS and a multi-agent retrieval proj…
RAG (Retrieval-Augmented Generation)
A technique that pulls relevant documents or CMS content at query time and feeds them into the model's context window, so responses reflect indexed knowledge rather than training data. Alex uses this at a practical, production level — currently active across two projects.
Primary use case
Alex OS relies on RAG to answer questions about this site's content accurately. Instead of the language model drawing on its own weights, queries are matched against indexed CMS content first — the retrieved chunks become the factual basis for each response.
Where you can see this in practice
Two projects demonstrate this directly: Alex OS — a FastAPI reasoning and retrieval layer that grounds answers in live CMS data — and Multi-Agent RAG, which extends the pattern across cooperating agents. Both are linked from this site if you want the specifics.