Jev is a Gamechanger! · Clief Notes

Jev is a Gamechanger!

What is Jev? A newly released decision model: you give it text and a set of options, and it tells you which option fits and how sure it is. And it does so extremely fast and cheaply.

I did several tests. This is what I saw.

In my own test it labelled 14,007 emails in 4 minutes for about $0.50. Running the same job through Sonnet would have cost about $42 and taken a lot longer. On a smaller run the answers were as good while being 80 times cheaper and fast enough you could use it practically in real time.

Why does any of this matter? Let me explain in an example of a workflow that would crank on this:

You have incoming email. Send it through a decision tree:

- Is the email for support or finance? - Jev

- If support, what product is it for? - Jev

- If for a TV, is it about the recall, or which of 3 TVs? - Jev

- If recall, send canned message - code

- Depending on which TV, assign to X person - code

- Search documents based on the question and draft a first response - LLM

The expensive LLM no longer does all the decision tree work it would have in the past. Jev does that for about 1/80th of the cost. The LLM (Or Human) is at the end of the tree not spending time pointing it in the right direction.

The recall is an example of adding a new or temporary category. With Jev that means writing one more sentence describing it. No retraining, no labelled examples. If it's a recall, send them where to go for it and no LLM is called at all.

It's cheaper than it looks. Questions about the same email can all go in one call: support or finance, which product, recall or not. The whole top of the tree comes back in one round trip. Only the LLM step at the bottom has to wait.

Where it needs care

There will always be ambiguity in text. LLMs and Jev are susceptible to this. Jev returns a confidence score that you can choose how to interpret. Is 70% good enough to move to sending a response before a human looks at it? It's cheap enough to run on every email even when a human still reads them all. It sorts the pile and flags the unsure ones first.

This is great for tuning, too. Does the human agree with Jev's assessment? Can we improve the question to get better confidence?

Jev reads what the text claims. An email that says "this is about the recall" gets the recall reply whether it's true or not. Anything where someone might be trying to game the system needs a check behind it.

Simply put: Jev fits in and makes both Code and LLM work better

- Code: rules you can write down as if X then Y

- Jev: a judgment you can describe but can't write as an x then y rule.

- LLM: anything that needs writing, or thinking through consequences.

https://typesafe.ai/

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Jev is a Gamechanger!