Asks a yes/no question and returns the probability that the answer is yes
, from 0 (no) to 1 (yes).
Returns noul (0–1). No separate confidence: two outcomes are fully described by one number.
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| PR# | Title | Type | Chance it breaks | Impact if it breaks | Main risk |
Reach | Explained | Test evidence |
Look first |
Reviewer | Advice | Jev cost |
|---|
Each cell shows Jev’s most likely answer and its probability. Hover a cell for the probability of every option, a column heading for the exact question, or a cost for its breakdown. Jev also sees computed facts: file types changed, areas touched, labels or branch, and flags like CI or dependency changes. What each column means.
Jev is a judgment model, not a text generator. It reads a state (here: computed PR facts, the title, the description and the diff) and answers typed questions in a single call. Each question’s type fixes the shape of its answer. TypeSafe’s docs define three. The examples on each card are real answers from the response below.
TypeSafe borrows the name “System One” from the contrast between fast, intuitive System 1 judgment and slower, deliberate System 2 reasoning. Jev is built for the first kind: quick, inexpensive judgments over structured state. Here it screens every PR with narrow questions and probabilities, leaving deeper investigation to reviewers when the answers are uncertain or risky.
Asks a yes/no question and returns the probability that the answer is yes
, from 0 (no) to 1 (yes).
Returns noul (0–1). No separate confidence: two outcomes are fully described by one number.
Selects one option from a set that has no order between them
, and returns the most probable option.
Returns choice, probabilities for every option (summing to 1), and confidence: high when one option peaks, low when it’s spread flat.
Rates the state against ordered, descriptive levels
, for when the answer is a position on a spectrum.
Returns score, probabilities per level, a legend and confidence. The score is an average: each level number times its probability, added up.
state plus the single longest questionstate plus all 14 questions togetherThose are Jev’s limits per TypeSafe’s Models page. Jev reads the state once and answers every question against it. When a PR’s text fits the budget, Jev reads it whole. When it doesn’t, the worker trims the diff hunk by hunk rather than dropping whole files. Every changed file keeps its header, so Jev still sees the full shape of the change. Hunks are then added one per file per round, source first, then migrations, CI and dependencies, then tests, config and docs, with lockfiles, build output and binaries last, so one huge file can’t crowd out the rest. Each file notes how many hunks were left out, and because missing diff can change the answers, the row gets a ! Trimmed badge saying how much Jev read, with a matching ! beside its advice. A long description is shortened to about an eighth of the budget first. Putting behavior first also helps accuracy: TypeSafe notes Jev suffers from context rot, so unrelated material in the state costs you accuracy.
The budget assumes 2.75 characters per token: pessimistic for prose, but close for a diff of mostly file paths and hashes, so a PR near the limit can still be rejected.
| Column | Type | Levels (low to high) or options | Question Jev answers |
|---|
If the answer is a position on a spectrum, use a Score.