[steve_adams_86]

I’ve found using logic trees with LLMs isn’t necessarily a problem or a deficit. I suppose if they were truly magical and could intuit the right response every time, cool, but I’d always worry about the potential for error and hallucinations.

I’ve found that you can create declarative logic trees from JSON and use that as a prompt for the LLM, which it can then use to traverse the tree accordingly. The only issue I’ve encountered is when it wants to jump to part of the tree which is invalid in the current state. For example, you want to move a user into a flow where certain input is required, but the input hasn’t been provided yet. A transition is suggested to the program by the LLM, but it’s impossible so the LLM has to be prompted that the transition is invalid and to correct itself. If it fails to transition again, a default fallback can be given but it’s not ideal at all.

However, another nice aspect of having the tree declared in advance is that it shows human beings what the system is capable and how it’s intended to be used as well. This has proven to be pretty useful, as letting the LLM call functions it sees fit based on broad intentions and system capabilities leaves humans in the dark a bit.

So, I like the structure and dependability. Maybe one day we can depend on LLM magic and not worry about a team understanding the ins and outs of what should or shouldn’t be possible, but we don’t seem to be there yet at all. That could be in part because my prompts were bad, though.

[tomhallett]

Any recommendations on patterns/approaches for these declarative logic trees and where you put which types of logic (logic which goes in the prompt, logic which goes in the code which parses the prompt response, how to detect errors in the response and retry the prompt, etc). On "Show HN" I see a lot of "fully automated agents" which seem interesting, but not sure if they are over-kill or not.

[spdustin]

Personally, I've found that a nested class structure with instructions in annotated field descriptions and/or docstrings can work wonders. Especially if you handle your own serialization to JSON Schema (either by rolling your own or using hooks provided by libraries like Pydantic), so you can control what attributes get included and when.

[steve_adams_86]

The JSON serialization strategy worked really well for me in a similar context. It was kind of a shot in the dark but GPT is pretty awesome at using structured data as a prompt.

[steve_adams_86]

I actually only used an XState state machine with JSON configuration and used that data as part of the prompt. It worked surprisingly well.

Since it has an okay grasp on how finite state machines and XState work, it seems to do a good job of navigating the tree properly and reliably. It essentially does so by outputting information it thinks the state machine should use as a transition in a JSON object which gets parsed and passed to a transition function. This would fail occasionally so there was a recursive “what’s wrong with this JSON?” prompt to get it to fix its own malformed JSON, haha. That was meant to be a temporary hack but it worked well, so it stayed. There were a few similar tools for trying to correct errors. That might be one of the strangest developments in programming for me… Deploying non-deterministic logic to fix itself in production. It feels wrong, but it works remarkably well. You just need sane fallbacks and recovery tactics.

It was a proprietary project so I can’t share the source, but I think reading up on XState JSON configuration might explain most of it. You can describe most of your machine in a serializable format.

You can actually store a lot of useful data in state names, context, meta, and effect/action names to aid with the prompting and weaving state flows together in a language-friendly way. I also liked that the prompt would be updated by information that went along with the source code, so a deployment would reliably carry the correct information.

The LLM essentially hid a decision tree from the user and smoothed over the experience of navigating it through adaptive and hopefully intuitive language. I’d personally prefer to provide more deterministic flows that users can engage with on their own, but one really handy feature of this was the ability to jump out of child states into parent states without needing to say, list links to these options in the UI. The LLM was good at knowing when to jump from leaves of the tree back up to relevant branches. That’s not always an easy UI problem to solve without an AI to handle it for you.

edit: Something I forgot to add is that the client wanted to be able to modify these trees themselves, so the whole machine configuration was generated by a graph in a database that could be edited. That part was powered by Strapi. There was structured data in there and you could define a state, list which transitions it can make, which actions should be triggered and when, etc. The client did the editing directly in Strapi with no special UI on top.

Their objective is surveying people in a more engaging and personable way. They really wanted surveys which adapt to users rather than piping people through static flows or exposing them to redundant or irrelevant questions. Initially this was done with XState and no LLM (it required some non-ideal UI and configuration under the hood to make those jumps to parent states I mentioned, but it worked), and I can't say how effective it is but they really like it. The AI hype was very very strong on that team.

[necmttn]

I'm building a whole AI agent-building platform on top of Xstate actors. Check it out craftgen.ai or https://github.com/craftgen/craftgen

[sghiassy]

LangGraph