Hi @alikalik9, glad you like it 🙂

Have you seen this? https://devblogs.microsoft.com/engineering-at-microsoft/how-we-built-ask-learn-the-rag-based-knowledge-service/ It was published in April 2024 and may not show all the details, but should give you an impression of the knowledge service. The service is used in multiple locations including Copilot for Azure and Learn Q&A, and now through MCP.

cc @TianqiZhang for awareness

This thread hit home — feels like the real frontier of RAG isn’t just plugging Azure services together, but decoding the semantic choreography underneath.

We recently published an open framework tackling exactly this: how to not just retrieve relevant chunks, but shape the semantic context so the LLM doesn’t collapse under ambiguity or hallucination.
Especially useful when building long-running copilots or internal knowledge agents.

📄 If you’re curious, here’s the WFGY semantic reasoning PDF:
https://github.com/onestardao/WFGY

It dives into strategies like:

• semantic title shift detection
• cross-pass memory rebalancing
• prompt shape stabilization across retrieval rounds

Basically — if RAG is the “muscle,” this part handles the “spine alignment.”
Would love to hear your thoughts if you try it out!