LatentEval

INSTRUMENT | Reliability testing

Release Gate Designer: Go/No-Go Rules and Decision Log

3 cited sources

Write the rule that turns your eval figures into PROMOTE, HOLD or ROLLBACK, get the verdict plus the rule that produced it, and keep a dated decision log and memo in your browser.

A release gate is a written rule that turns eval figures into one of three decisions. Write the rule here and keep the decision. This page runs no suite.

You get a verdict, the rule behind it, and a line to paste into the release channel. One number cannot say which rule failed, so there is none.

Built on our work on whether a difference is real and what a pass rate hides. Pairs with the change-impact worksheet.

1. The gate

Who owns this gate

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Who says ship or not.

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Who may grant an exception. Required before an override on a ROLLBACK saves.

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What each state means here

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Rules

Five editable examples to start from. Every threshold here is yours to set: nothing on this page fills one in for you. On a regression rule, Baseline and Observed take the two arms' own figures, and the p-value field takes the p-value from a paired significance test, an A/B comparison, or an independent two-proportion test when the two arms ran on different cases. A rank-uncertainty check answers a different question: it gives you a position in a ranking, and no field on this page takes a rank, so read it as context for whoever owns the decision.

Rule 1

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Required alongside a p-value: name the test it came from.

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Rule 2

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No default on a regression rule. The wrong orientation passes a worse release in silence.

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A value ending in % is read as a rate. 15000 stays 15000.

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Required alongside a p-value: name the test it came from.

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Rule 3

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No default on a regression rule. The wrong orientation passes a worse release in silence.

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Points and percent of baseline can return opposite verdicts on the same numbers.

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A value ending in % is read as a rate. 15000 stays 15000.

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In this rule's own unit. Needed before a p-value can soften a state.

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From your own test. This page runs none.

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Required alongside a p-value: name the test it came from.

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Rule 4

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A value ending in % is read as a rate. 15000 stays 15000.

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Required alongside a p-value: name the test it came from.

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Rule 5

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Required alongside a p-value: name the test it came from.

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Baseline cited

The version label your golden set carries.

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Bring evidence in

Three files, each read in your browser and none of them sent anywhere. Everything else is typed.

A run card from the eval run register

Fills observed and baseline on one regression rule, matched by variant id, and proposes a direction for you to confirm.

JSON. Drag one here, or use the box below.

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An export from the error budget

Fills the observed remaining budget on one budget rule. A class reading breach fails that rule whatever the total says.

JSON. Drag one here, or use the box below.

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A frozen-baseline manifest from the golden-set version tracker

Fills the citation below and no rule value. A frozen metric is cited here, never compared.

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Mandatory rules not passing

1

HOLD. Task success regression did not evaluate.

HOLD: evidence missing for Task success regression

1 of 5 mandatory rules does not pass: 1 did not evaluate.

Every rule, as written, with what it read

RuleSuiteRule as writtenObservedResultOn failure
Destructive-action safety cases safety-critical no case in this suite may fail 0 failing pass
Task success floor core-task observed at or above 0.8 0.85 pass
Task success regression core-task a regression rule with its direction or unit unset 0.842 not evaluated hold
Remaining error budget slo remaining at or above 0 1 pass
Rollback or compensation path release-ops an artifact is named revert to previous tag pass

2. The baseline this cites

Dataset version

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Frozen on

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Nothing imported yet. A regression rule still evaluates without this; the memo then records the baseline set as unnamed.

A frozen value that arrives here is cited, never compared. Freezing one is the version tracker's job, and a figure moves into a rule above only because you typed or pasted it.

3. The decision, and the record

This decision

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Exception

Required when an override is granted. On a ROLLBACK the override owner above is required too.

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Decisions stay in this browser and go nowhere else. Up to 200 of them; after that, export and clear.

Decision log

Date Candidate Baseline Verdict Deciding rule Decided by Override Dataset version
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Export the log

Bring a log back

Reads a log you exported from this page and replaces what is here. Export the current one first: replacing is not a merge.

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verdict = the worst on-failure state among the rules that failedHow?

How this is calculated

Two steps, and neither one combines rules. Each rule is judged on its own terms: a must-pass rule passes when its failing-case count is zero; a floor compares one observed value against one threshold; a regression compares the movement from a baseline against a tolerance, in points or as a percent of that baseline; a budget compares what is left against a line; an evidence rule passes when an artifact is named.

Then the verdict is the worst on-failure state among the rules that failed, and the deciding rule is the first one at that level in your own row order. A rule that could not be evaluated counts as failing and holds. Every comparison is inclusive and carries a small tolerance for floating point, so a deterioration of exactly 0.020 against a tolerance of 0.020 passes rather than failing on a value the machine stores as 0.020000000000000018.

A rule can only soften from roll back to hold when both of two things hold: your p-value is above your alpha, and the movement is inside the noise floor you stated. A p-value on its own is not evidence that nothing changed.

No number here spans more than one rule. Four rules passing out of six is reported as a count of what failed, never as a proportion, because a proportion cannot say which rule it was.

Formula: verdict = the worst on-failure state among the rules that failed

Gate lines other people have published

One entry. It is one team's convention on one system, shown so you can see what a published line looks like. Nothing here is filled into a rule above.

  • Rollback line at 70 percent of the target

    As stated: a build is rejected outright when any one dimension falls below 70 percent of its own target, for example below 56 percent where the target is 80 percent.

    Claimed for one team’s staging gate on one internally deployed multi-agent assistant, across 38 evaluation runs over four weeks, with five named quality dimensions.

    Automated Self-Testing as a Quality Gate (arXiv 2603.15676), read 2026-08-27

    One team’s line on its own system. It is not carried over to yours, and this page never fills it into a rule for you.

Questions

Questions

Why will this not give me one number?

Because one number cannot tell you which rule failed, and that is the only part of a gate a release channel can act on. A page that averaged six rules into a percentage would let a failed safety case hide behind five passes.

Points or percent of baseline?

They disagree. A drop from 0.860 to 0.842 is 0.018 points, which passes a 0.020-point tolerance, and 2.09 percent of the baseline, which fails a 2-percent one. Pick the one your team argues in, and write it down.

Why does a regression rule refuse to evaluate until I pick a direction?

Because the wrong orientation passes a worse release quietly. On latency, higher is worse; on task success, higher is better. There is no safe default, so the rule holds instead of guessing.

Does this run my suite?

No. It reads figures you bring, from your own runs. There is no repository field, no workflow hook, and nothing to connect.

Where do the decisions go?

Into this browser, and nowhere else. Export them before you clear the browser, change machine, or hand the record to anyone.

Sources

  1. LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG ApplicationsarXiv Retrieved
  2. Canarying Releases (Site Reliability Workbook)Google SRE Retrieved
  3. Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM ApplicationsarXiv Retrieved