@mstockton: Some raw / unfiltered thoughts around the Jev model. Thinking out loud: - Lots and lots of problems in business are cla…

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Summary

The author discusses the use of classical ML versus LLMs for business classification and regression problems, evaluates the Jev model as a potential tool, and compares it with existing techniques like Structured Outputs and DSPy, highlighting accessibility and effectiveness concerns.

Some raw / unfiltered thoughts around the Jev model. Thinking out loud: - Lots and lots of problems in business are classification or regression problems. Lots and lots of companies don't know that the types of problems they have are solvable by classical ML methods. They often solve them with people and process instead of technology. - With the emergence and popularity of LLMs, more companies are thinking 'Maybe we can use AI for that!' and are solving classification and regression problems with LLMs. - This is good in some ways, because companies are potentially automating some manual work, but also bad in some ways, because it's often the wrong tool for the job, and possibly not as good as the classical ML methods for what they are trying to do.. - If anything, the emerging popularity of 'AI' (which for most folks is synonymous w/ LLMs at this point) has caused them to try to solve their problems with that type of 'AI' (even if classical methods are more appropriate if you knew about the available tools / techniques) - There are very established 'classical ML' patterns to solve the problems they have (e.g. XGBoost is often all you need). AutoML works pretty well at this point for the 80% of problems here too, as long as your data is clean. - But the classical techniques require you to label your data, train a model, and host that model somewhere. They aren't as easy to use compared to 'calling an LLM API' - and it requires you and your org to be aware of those techniques, and capable of investing in them. - Classical ML is now much easier to do because of LLMs. I've built a bunch of models in the last 2 years using LLMs to build the classical ML code, do data cleaning, etc. - that would have taken me *much longer* if I didn't have an LLM to help - but still harder than shoving the problem at a text-based model. - There are some great patterns / techniques which exist around LLMS to constrain output for classification / regression, and make things cheaper / more performant (Structured Outputs, DSPy, GEPA, etc.) - still not super-accessible to the broader audience (I can count on one hand the number of practioners I know who have experimented with DSPy / GEPA -- maybe a few more who use Structured Outputs) - I am hopeful that these types of things become more adopted, but maybe the technical hurdles are too high in the short-term. - With Jev, maybe they've nailed the 'UX' for this. Yes, it looks a lot like structured outputs, but it is more opionated and directly targets classification / regression -- in a way that probably more folks will understand vs. Structured Outputs / DSPy / GEPA - I do wonder how effective it will be. It feels very adjacent to zero-shot Timeseries stuff like TimesFM. Like yes, it is very cool you can do predictions without training, but when you look at stuff like TimesFM, it seems to be not very good compared to supervised training of TS models (again, requires expertise to do so). Maybe you can put your labeled data in context, and this will work - But then my question becomes -- if it takes a bunch of know-how to use this type of zero-shot classifier / regressor model, at what point is it better to just use LLMs to help you build classical ML models? Thank you for reading my rant. I know it is disorganized and probably not fully coherent. Either way, it's super-cool to see new ways to piece together this new tech that has emerged over the last few years. Excited to track where Jev goes, and I think it prob. will be useful for some. I plan on trying it out for sure.
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Cached at: 09/16/26, 10:02 AM

Some raw / unfiltered thoughts around the Jev model. Thinking out loud:

  • Lots and lots of problems in business are classification or regression problems. Lots and lots of companies don’t know that the types of problems they have are solvable by classical ML methods. They often solve them with people and process instead of technology.
  • With the emergence and popularity of LLMs, more companies are thinking ‘Maybe we can use AI for that!’ and are solving classification and regression problems with LLMs.
  • This is good in some ways, because companies are potentially automating some manual work, but also bad in some ways, because it’s often the wrong tool for the job, and possibly not as good as the classical ML methods for what they are trying to do..
  • If anything, the emerging popularity of ‘AI’ (which for most folks is synonymous w/ LLMs at this point) has caused them to try to solve their problems with that type of ‘AI’ (even if classical methods are more appropriate if you knew about the available tools / techniques)
  • There are very established ‘classical ML’ patterns to solve the problems they have (e.g. XGBoost is often all you need). AutoML works pretty well at this point for the 80% of problems here too, as long as your data is clean.
  • But the classical techniques require you to label your data, train a model, and host that model somewhere. They aren’t as easy to use compared to ‘calling an LLM API’ - and it requires you and your org to be aware of those techniques, and capable of investing in them.
  • Classical ML is now much easier to do because of LLMs. I’ve built a bunch of models in the last 2 years using LLMs to build the classical ML code, do data cleaning, etc. - that would have taken me much longer if I didn’t have an LLM to help - but still harder than shoving the problem at a text-based model.
  • There are some great patterns / techniques which exist around LLMS to constrain output for classification / regression, and make things cheaper / more performant (Structured Outputs, DSPy, GEPA, etc.) - still not super-accessible to the broader audience (I can count on one hand the number of practioners I know who have experimented with DSPy / GEPA – maybe a few more who use Structured Outputs) - I am hopeful that these types of things become more adopted, but maybe the technical hurdles are too high in the short-term.
  • With Jev, maybe they’ve nailed the ‘UX’ for this. Yes, it looks a lot like structured outputs, but it is more opionated and directly targets classification / regression – in a way that probably more folks will understand vs. Structured Outputs / DSPy / GEPA
  • I do wonder how effective it will be. It feels very adjacent to zero-shot Timeseries stuff like TimesFM. Like yes, it is very cool you can do predictions without training, but when you look at stuff like TimesFM, it seems to be not very good compared to supervised training of TS models (again, requires expertise to do so). Maybe you can put your labeled data in context, and this will work
  • But then my question becomes – if it takes a bunch of know-how to use this type of zero-shot classifier / regressor model, at what point is it better to just use LLMs to help you build classical ML models?

Thank you for reading my rant. I know it is disorganized and probably not fully coherent. Either way, it’s super-cool to see new ways to piece together this new tech that has emerged over the last few years. Excited to track where Jev goes, and I think it prob. will be useful for some. I plan on trying it out for sure.

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