I've found that they work great as gates ("does this require a human review?") or filters ("is this suspicious?") and occasionally as judges (based on this context, is the expected pain score for this user above 7.5/10?).

Yep, I use pi-warden (https://github.com/DevMortimer/pi-warden) as part of my workflow.

I used it to classify 10k ish repos for https://hnrepos.com/

I like that it’s fairly deterministic, with a bit of variation. Think very low temperature rather than perfectly repeatable. I just didn’t want the randomness that comes from token by token execution even at low temps.

I’ve also used it for “does this contain an entity”, and then I run an LLM to extract the entities. Saves on the LLM cost.

I adopted it as a super-fast credibility scan on the text (both the online version and the offline decision api inside chrome.

https://chromestatus.com/feature/5155080092385280 https://evergreen-labs.org/products/credibility-analyzer

I'm using them to label training data for model development. You can do something like label a bunch of records with Jev, then train the model, then check results with opus and a hold-out set.

Selecting between user-provided tags to provide quick suggestions, semantic search, that sort of stuff.

Task classification - given a set of classes, return the most likely class for each task

Guardrails - check the text against a set of guardrail checks (questions)

Search - Given a search criteria, I first have an LLM generate a query that would go to the decision model. This query is a rubric that scores each document. The highest scoring documents become the search hits. This is fast and cheap, so you can use it as a cross-encoder for your entire set, instead of just as a final re-ranker.

> How are people using them at the moment?

We use it for our critique looping within our own harness.

But decision models can do things like write SQL queries, if you're willing to put a little bit of trickery upfront.

My team's writing a paper about it for ICDE 2027 as we speak (as a follow-up to the DICE demo/paper from VLDB 2014).

But my Linkedin post[1] has a quick summary of the idea, without explaining everything we do there.

[1] - https://lnkd.in/p/gAsWcrCd

Does anyone use it to classify LLM harness tool requests? Between allow, ask, deny.

Here is a recent case study on this: https://news.ycombinator.com/item?id=50436640

Tool routing. For example which MCP or Skill should i use for a given question.

Evals. is this hitting the criteria or not? (Test case passes or fails)

Game modding. I wanted to create a decent opponent AI for w40k on tabletop simulator.

I work in manufacturing and would like to use LLMs for scenarios where there's obviously still rules for what should happen next, but there's just so many variables that I can't just code it into a decision tree easily.

As an example: I could have something I'm building fail. If I take a picture of it, can I see that it's

1) a false failure

2) salvageable

     2a) decision-tree for *who* to send it to for reworking things

3) scrap

As I think of the above decision tree, I see some problems where I'm just asking myself, "why wouldn't I just use <insert a lot of options you can probably guess here>?".

I'll be honest, I think right now decision models are in a "solution looking for a problem stage", but I think there's opportunity still.

Email filtering, ticket triage/categorization/etc.

Yes, literally built my own version of /goal in Claude/codex today for pi using jev. My agent was stopping so jev is called each turn to see if it’s done or not - similar to how it works in Claude, according to Claude…

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