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>> the hard part is contextualizing it in a way it is immediately useful. That depends on understanding the company/domain lingo, understanding users, etc.

How do you control this deterministically? It sounds like the "hard part" is variation in prompting & selectively choosing the right data to include, both of which I could see being good enough right now but hard to deliver definitively.

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Being able to filter down the data deterministically is a big value add, especially as the number of documents scale into the range of multiple millions. We have filters by document-set, tags, time range, source type (ie. only include Slack + Google Drive, or Confluence + Jira + Gong, etc.)

The challenge is with the non-deterministic portions of the flow as you pointed out. Ensuring retrieval quality in out-of-domain datasets, guardrailing the LLM generation, working with conflicting or deprecated information are some of the interesting areas we're addressing. Happy to dive deeper on any aspect you're curious about, and I'm sure we can learn from the discussion as well.

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