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Core Concepts

Decomposition

Breaking a broad judgment into one question per factor, then combining the answers with weights and rules you control in code.

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docs.typesafe.ai
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2026-09-24

Definition

Instead of "rate this startup pitch", ask separately about market size, technical feasibility, and differentiation, then combine the answers with your own formula. When priorities shift, change a coefficient in code rather than rewriting a prompt.

Decomposition does not require more round trips: questions over the same state run in parallel within one request, so the split costs only the tokens for the extra questions.

Tagspracticesdesign

Related terms

Definitions that connect to this one.

Atomic questionA question that asks one specific, well-scoped thing — the kind of judgment a knowledgeable person makes in a few seconds.Core ConceptsComposite scoringBreak a judgment into atomic Score questions, normalize each, and combine them with weights you control in code.PatternsParallel questionsBatching many questions into one request instead of many calls, which is dramatically cheaper and faster with identical answers.PatternsSystem Two taskWork that needs extended, multi-step reasoning; the docs treat it as a signal to decompose rather than a job for Jev.Core Concepts

From the directory

How to Build with TypeSafe
Design AI-powered software by keeping code in control and giving System One narrow, structured decisions to make.
Practices & PatternsDocs#official#patterns#architecture
Official
Pattern: Composite Scoring
Break a judgment into atomic Score questions, normalize each by its top level, and combine them with weights you control in code, as in the resume example's four scored dimensions.
Practices & PatternsDocs#official#patterns#score
Official

From the community

Posts from builders shipping with Jev right now.

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Jev benchmarked on two public safety corpora

Gaurav Gosain
Gaurav Gosain
@_GauravGosain
·

1/ Benchmarked TypeSafe's Jev on two public safety corpora. It doesn't generate text, it returns calibrated probabilities you threshold in code. 96.5% on prompt injection, all 662 messages in deepset/prompt-injections. No tuning. 325ms p50.

Terminal dashboard showing 96.5% accuracy on prompt injection and 89.0% pairwise on vulnerable code, with a context ablation and calibration plot.
Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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An open 151M-parameter decision engine

Hemant
Hemant
@heman10x
·

TypeSafe AI came out of stealth with Jev, and access is behind a waitlist. I built an open source version Verdict (Open-jev) you can run right now in a browser tab: And its a real post trained model..(link in comments) It is a post trained 151M parameters model. ModernBERT-base Show more

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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Agent Handoff Gate: agents verify what they hand off

A local Telegram analyzer with Jev as the classifier

Dagmawi Babi
Dagmawi Babi
@DagmawiBabi
·

I vibe-coded this simple, open-source and local Telegram content analyzer using Jev as a classifier. Jev Classifier • jevclassifier.vercel.app Export your channel data as JSON and it will analyze each post's intent, quality, sentiment, and reaction tone and if it's a DM/Group Show more

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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A Krunker-inspired FPS, decided by Jev

Neel Patel
Neel Patel
@Neel490
·

Maybe I’m just washed, but Jev is beating me at my own game. Built a Krunker-inspired FPS where you 1v1 Jev. Try it out yourself: jev-arcade.vercel.app/duel

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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Pixel art out of 256 multiple-choice questions

Aryan Singh
Aryan Singh
@just_aryansingh
·

I turned @typesafeai Jev model into a pixel art model. 256 multiple choice questions, one per pixel, plus refinement passes. These were the best results out of the bunch I tested. Code: github.com/Wizhill05/type…

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

Watch on X
Reply
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