Typed Decisions with Jev (and Laya)
Cheaper, faster decisions inside LLM systems: what typed-decision models are, where they really save tokens and money, where they do not, how Jev compares with the open-weights Laya, and what is proven versus not, dated 25 September 2026.
Course modules
01Start here: what Jev is, and how well proven it is
A typed-decision model answers with a value your code can branch on, not a paragraph. Here is what that means, why it might cut LLM cost, and the honest state of the evidence on 25 September 2026.
What a typed decision is
The three answer types (choice, score, yes/no probability), what goes in, what comes out, and a runnable stand-in you can use to learn the shape.
Does it really save money?
Where the token and cost savings come from, when they are real, when they are not, and how to do the sum honestly with a calculator.
Use case: the triage cascade
Route high-volume incoming items with a typed decision, escalate the unsure ones, and keep humans in charge of anything that touches money.
Use case: guardrails and approval triage
Use a fast risk check in front of an agent's tool calls, fail closed, and never let a low score override a hard rule or a human gate.
Use case: decompose, filter, route
Three cheap moves that save LLM tokens: split a hard question into small ones, filter context before the LLM sees it, and route to the right model.
Trusting the numbers: calibration and shadow tests
A confidence number is a claim, not a fact. How to test it, correct it, and run a typed-decision model beside your real process before it decides anything.
When not to use it
Hard stops, cautions and cheaper alternatives: every consideration that should make you keep a decision away from a typed-decision model.
Jev vs Laya: hosted API or open weights
Two typed-decision models with the same idea and opposite deployment: a managed API and an open-weights model you run. When to use which, and what can go wrong with each.
How it could help your projects
Concrete places a typed-decision layer could fit real projects: a personal site with agents, a small SaaS, and consulting work. Honest about what is a proposal, what it would save, and where it does not belong.
Study guide: cheat sheet, glossary, quiz, sources
One page to revise from: the key ideas, a glossary, a self-test, what to re-check, and the annotated list of sources with how far to trust each.
Where to go next
Agentic AI Harness Patterns
What is an AI agent harness, and why do ten named patterns keep it out of trouble? A field guide built on real incidents, a running example, and an interactive playground for every pattern.
AI Ops
The difference between a demo and a product that survives real users: monitoring, cost control, security, guardrails, and rollback. The operational layer that keeps AI systems safe, affordable, and trustworthy in production.