Skip to content
JevDirectory.org
Core ConceptsPrimitive

AI primitive

One of TypeSafe's small, typed building blocks — Choice, Score, Noul — that pair a question with a typed answer you compose in code.

Category
Core Concepts
Also known as
Primitive
Related terms
5
Directory entries
8
Docs
docs.typesafe.ai
Added
2026-09-24

Definition

TypeSafe exposes three AI primitives. Like software primitives, they are meant to be modular, composable, structured, reliable, and fast. Each asks a different type of question and returns a different shape of answer.

The point of primitives is that reliability comes from smallness. A broad judgment hides several decisions behind one answer; a primitive exposes one judgment your code can inspect, weight, and route on.

Tagscoreprimitives

From the directory

The three TypeSafe question types, the typed answers they return, how to choose between them, and how to ask several in a single call.
Practices & PatternsDocs#official#primitives#choice
Official
The idea behind Jev: models that return typed decisions and probabilities instead of generated text, named after Kahneman's fast-thinking System 1, with confidence to route around uncertainty.
Sites & GuidesDocs#official#docs#architecture
Official
The raw HTTP contract behind every SDK: POST a state plus typed noul, choice, and score questions to /v1/systemone, and get one answer per question, with error codes and retry guidance.
Sites & GuidesDocs#official#docs#api
Official
Pick one option from a set you define. Returns the option, the full probability distribution, and 0-1 confidence, with up to 255 options and parallel questions that barely add latency.
Practices & PatternsDocs#official#primitives#choice
Official
Rate content against 2 to 10 ordered levels. Returns a probability-weighted score that can land between levels, per-level probabilities, and confidence, with the arithmetic left to code.
Practices & PatternsDocs#official#primitives#score
Official
The yes/no primitive: one probability from 0 to 1, where the value is the answer and no separate confidence is needed. True and false criteria pin down subtle boundaries.
Practices & PatternsDocs#official#primitives#noul
Official

2 more matching entries in the full directory.

From the community

Posts from builders shipping with Jev right now.

Follow @typesafeai

Vercel's fx safety reviewer, 18x faster

We're seeing extraordinary results from @typesafeai. Default mode in 𝚏𝚡 is auto, with a safety reviewer analyzing every command. That reviewer runs on GPT Luna today. Jev is up to 18x faster (p95) *and* more accurate. It's coming to @vercel AI Gateway and likely new default.

Pranit
Pranit
Vercel
@fazxes

We benchmarked fx auto mode (safety) classifier with @typesafeai's Jev. tl;dr: ~5-18x faster and more accurate than 𝚐𝚙𝚝-𝟻.𝟼-𝚕𝚞𝚗𝚊, our current top choice

Image
Reply

Jev lands on OpenRouter

700 leads scored for $0.09

Beating Gemini Flash Lite on an eval

Browser Use Ultrafast, powered by Jev

A really smart switch statement

hype-free explanation of jev: jev does not replace gpt / claude jev is just a *really* smart switch statement like if 2016 ml classifiers got 2026 levels of intelligence it's a new* type of tool that will make a lot of workloads insanely fast, cheap, and accurate * = and by Show more

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

Reply