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Comment by b2v | Hacker News Reader

Neat, thanks! I'm still pondering wether I should be using this since most of the retries I have to do are because of the LLM itself not understanding the schema asked for (eg output with missing fields / using a value not present in `Literal[]`) — certain models being especially bad with deeply nested schemas and output gibberish. Anything on your end that can help with that?

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nothing specific, but you can try our prompt / datamodel out on https://www.promptfiddle.com

or if you're open to share your prompt / data model with, I can send over my best guess of a good prompt! We've found these models works even with over 50+ fields / nested and whatnot decently well!

I might share it with you later on your discord server.

> I can send over my best guess of a good prompt!

Now if you could automate the above process by "fitting" a first draft prompt to a wanted schema, ie where your library makes a few adjustments if some assertions do not pass by have having a chat of its own with the LLM, that would be super useful! Heck i might just implement it myself.

[Another BAML creator here]. I agree this is an interesting direction! We have a "chat" feature on our roadmap to do this right in the VSCode playground, where an AI agent will have context on your prompt, schema, (and baml test results etc) and help you iterate on the prompt automatically. We've done this before and have been surprised by how good the LLM feedback can be.

We just need a bit better testing flow within BAML since we do not support adding assertions just yet.

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