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This puts a lot of LLM in front of the information discovery. That would require far more sophisticated prompting and guardrails. I'd be curious to see how people architect an LLM->document approach with tool calling, rather than RAG->reranker->LLM. I'm also curious what the response times are like since it's more variable.

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Hmmm, the post is an attempt to explain that Mintlify migrated from embedding-retrieval->reranker->LLM to an agent loop with access to call POSIX tools as it desires. Perhaps we didn't provide enough detail?

That matches what I'm curious about. Where an LLM is doing the bulk of information discovery and tool calling directly. Most simpler RAGs have an LLM on the frontend mostly just doing simpler query clean up, subqueries and taxonomy, then again later to rerank and parse the data. So I'd imagine the prompting and guardrails part is much more complicated in an agent loop approach, since it's more powerful and open ended.

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