A walkthrough of how to use Jev (a cheap, fast "decision model" sold by TypeSafe) rather than a chat LLM for the small fuzzy judgements inside software: routing tickets, classifying reviews, verifying claims. At roughly 4.2 cents per million input tokens and ~150ms latency, calls that were previously too slow or expensive become viable at scale. The video organises the space into five use-case categories, three decision types (null, choice, score), ten decision shapes, and four design patterns, then assembles them into a sample customer-support flow.
- Jev is framed as a decision model, not a chat model: you give it context plus a question and it returns something your code can branch on, not prose.
- Pricing of about 4.2 US cents per million input tokens with no output charge means a million 1,000-token requests costs roughly $42, which is what makes per-record and per-line checks practical.
- TypeSafe groups the opportunities into five categories: automation, real-time applications (Jev has been shown powering gameplay in Doom at ~150ms), large-scale data processing, verification, and harness engineering.
- There are three decision types: null returns a yes/no probability, choice picks from a supplied set, and score places the answer on an ordered scale; all return confidence signals you can act on.
- Ten decision shapes describe the job being done: classification, detection, scoring, routing, search, retrieval, ranking, verification, feature extraction for non-generative ML models, and structured data extraction.
- Search, ranking and retrieval are distinguished clearly: search finds candidates, ranking orders them, retrieval supplies what the next step needs.
- Four design patterns: speculative fan-out (ask all plausibly useful questions in one request since 10 questions cost nearly the same time as one), confidence-gated routing, composite scoring with weights you control, and intent routing at the entrance to multiple handlers.
- The speaker warns that you still need your own permission checks, confirmation rules and business logic; don't just trust the decision model's output.
- To get started: take a workflow you already understand, find where software must interpret fuzzy natural language, ask the smallest useful question, and decide what happens when the answer is uncertain.
The concrete cost math is the whole argument for why these fuzzy micro-decisions can now be run across entire collections of records.
Let's suppose a request uses a thousand input tokens, including the context and the questions. If you run a million of those requests, it would only cost you $42 in Jev token charges.
A counterintuitive but immediately actionable design rule: over-ask in one request and discard the answers you don't need.
That's why it's called speculative — because the time it takes to ask one question versus 10 questions in a single request is virtually the same. So you're better off to ask the questions that you might need to avoid a series of round trips with the model.
Jev isn't a chat model, it's a decision model. You give it some context and a question and it returns an answer that your software can act on.
That probability matters because a result near halfway means the model is uncertain about yes or no.
A quote can exist and still be used to make the wrong claim.
Don't just trust what this decision model is going to tell you. You still need to properly secure and gate everything on your own side using your own business logic.