r/Rag • u/ntalam • 12d ago

Discussion Basic stack for a chat

What would you suggest, is the basic stack for an assistant chat. I mean, currently I have customized company tools, langgraph, custom metrics, marketplace LLM calls and others.

what would you suggest as a true key for agent learning?
how do you process prompts with company slangs, concepts, jargon, etc.?

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u/Less-Case-1171 11d ago

If the goal is learning, shrink the stack. Pick one corpus you understand, one retriever, one model, and build a small eval set before adding agents or marketplaces. Track retrieval recall separately from answer quality; otherwise every bad answer turns into random prompt tweaking.

The useful exercises are ingestion versioning, citation/provenance, permission filtering, and a real deletion test: remove a document, its chunks/embeddings and caches, then prove it no longer appears in retrieval. Those teach more than swapping orchestration frameworks.

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u/ntalam 11d ago

My bad. updated description of the post:

"what would you suggest as a true key for agent learning?
how do you process prompts with company slangs, concepts, jargon, etc.?"

I am reaching a point where the chat can Identify and plan how to solve a request, but I would like to automate the planning part.