Prompt & AI Workflow Enablement

Prompt Evaluation & QA

Prompt changes are ready for release only when every version runs against fixed cases, recorded settings, shared rubrics, and calibrated human review.

A prompt edit that dazzles in one chat can quietly break three other tasks. We give prompt changes a stable baseline, representative cases, explicit rubrics, calibrated reviewers, failure slices, versioned experiments, and a regression gate, so an improvement is something you can show, not remember.

A prompt-case inventory, reviewer-calibration notes, versioned failure results, and a regression log settle which prompt can ship, so an improvement is something you show rather than remember.

Discuss this workSee the evaluation service
Illustration of Prompt Evaluation & QA: a team testing and refining prompts against real examples

Some of the 500+ brands we've worked with

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  • EY
  • Shell
  • Sanofi
  • Ülker
  • Domino’s
  • Tazedirekt
  • Sportive

Steps, gates, and who decides

How we work

Every candidate faces the same cases, settings, rubric, and baseline. A version wins by surviving that comparison, not by producing one good afternoon of outputs.

  1. Define the task and baseline

    We agree representative inputs, expected behavior, known failures, critical slices, current prompt versions, and the release criteria.

    AI assist
    Known failures and current prompt versions become a draft test set for the prompt owner to trim.
    Human gate
    Does the test set represent the work the prompt must actually perform? Your prompt owner confirms the test set represents the real task.

  2. Run versioned experiments

    We compare prompt candidates on identical cases using deterministic checks, rubric graders, and recorded model and parameter settings.

    AI assist
    Candidates run against the fixed cases under tooling that records the version manifest for each experiment.
    Human gate
    Can every result be reproduced from the version manifest? Your evaluation lead confirms a result before it's compared across versions.

  3. Calibrate reviewers and slices

    Human reviewers score shared examples, resolve material rubric differences, and inspect critical failure slices separately from the average.

    AI assist
    Cases where reviewers scored the same example differently get flagged for resolution.
    Human gate
    Is reviewer agreement strong enough to choose between prompt versions? A human reviewer resolves the rubric difference before it affects the score.

  4. Approve the regression gate

    We select the approved version, record exceptions, and connect the test suite to the change policy for future prompt edits.

    AI assist
    The accepted version's results become a draft regression-gate record for the prompt owner.
    Human gate
    Does your prompt owner approve this version and its open exceptions? Your prompt owner approves the version and its open exceptions.

Named artifacts you keep

What you get

The team can compare a change, reproduce the result, and return to the approved version when an edit goes wrong.

  • Test evidence

    Prompt-case and model-setting inventory

    Links representative cases, prompt text, model settings, expected behavior, and known failures to each experiment.

  • Playbook

    Task rubrics and reviewer-calibration notes

    Defines what counts as success, failure, and reviewer disagreement for each task or slice.

  • Dashboard

    Experiment ledger and failure-slice report

    Compares candidates by case, slice, failure type, reviewer notes, and versioned result.

  • Decision record

    Approved prompt and regression-change log

    Records the released prompt, accepted exceptions, ownership, and the checks required before the next change.

Scope and honest limits

When to bring us in

Choose this when a few memorable chats, or whichever output looked best today, are standing in for a repeatable comparison between prompt versions.

A good fit when

  • A prompt wins a few memorable chats, but your team cannot tell whether the target task improved or only the style changed.
  • A known failure returns after an edit, because prompt versions and regression cases are stored in separate places.
  • Human reviewers score the same output differently, yet the shared rubric and calibration session cannot resolve the release question.
  • The target task is known, but failures, critical slices, baseline behavior, and representative cases are not defined as one test set.
  • Deterministic checks and rubric graders disagree, so human calibration cannot produce a reproducible experiment result.
  • A candidate prompt looks stronger on average, but failure analysis and the regression gate still leave the approved version undecided.
  • Prompt candidates see different inputs or review rules, so the team cannot reproduce a fair comparison between versions.

Better handled as other work when

  • You expect the regression gate to count as an audit opinion or certification. Legal and regulatory determinations still belong to your qualified authority.
  • You need one prompt to behave identically across every model, task, user, or future version, although this evidence covers only the tested settings.
  • You need ongoing prompt-lifecycle operation after the approved version is handed over, because this engagement ends at the recorded change policy.

If one of these is closer to your situation, start here instead: See the evaluation service

We operate the systems we test

It's hard to test a system well if you've never had to keep one running. We operate production AI ourselves, so our evaluation, security testing, and LLMOps work starts from what actually breaks. The people on it are senior engineers, and Zeo has been doing client work since 2011.

Content we've produced on this topic

Articleby Can MutioğluWhat is the Few-Shot Prompting Technique? How to Use It?Articleby Ezgi Gülsen YaylıGuidance with Chain of Thought in Artificial IntelligenceVideoby Ozan KetenciA Hands-On Guide to Elevating Efficiency with OpenAI's Custom GPT - Ozan Ketenci, ZeoArticleby Samet Özsüleyman7 Things AI May Struggle With in Content Creation

Tools we use

Tools behind this work

  • Agenta

    runs versioned prompt experiments side by side against a fixed baseline

  • PromptLayer

    logs every prompt version against the exact baseline it has to beat

  • Confident AI / DeepEval

    scores against rubrics and reports failure slices, not one aggregate number

  • Giskard

    automatically scans for the quiet regressions this page warns a prompt change can cause

  • Label Studio

    measures reviewer agreement directly, the calibration this page's bar requires

Next step

Compare the versions, awkward cases included

Bring the current prompts, representative work, and whoever owns the ship decision for the next version.
Talk to Zeo

Before you decide

Questions we hear a lot

The task, current prompt versions, representative inputs, expected behavior, known failures, policy and data constraints, baseline evidence, human reviewers, model settings, and release criteria.