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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTwo LLM outputs can both look plausible and still differ on factuality, completeness, instruction following, safety, or whether the task actually got done. A quick read mostly catches fluency and tone, which is where weak outputs often look strongest. An eval replaces that impression with a repeatable procedure: a defined task, explicit success criteria, a representative set of cases, a scoring method you can explain, human review to check the scoring, and the same run after every change.
Why “looks good” cannot be a release criterion
A visual check is a reasonable first observation. It becomes a problem when it is the only one. Two reviewers reading the same outputs may weigh different things, and a reviewer who knows which version produced which output tends to lean toward the one they expected to win. Memory makes it worse: the handful of examples you remember are usually the ones that went well.
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The gap shows up in specific dimensions that a skim tends to miss:
| Dimension | What a skim tends to miss | How to check it |
|---|---|---|
| Factuality | Confident wording that adds a detail the source never contained | Compare each claim against a reference document or verified answer |
| Completeness | An answer that handles the easy half of a multi-part request | Keep a per-case checklist of required parts |
| Instruction following | Wrong format, length, or prohibited content that is easy to overlook when the prose reads well | Use mechanical checks such as schema validation or banned-term matching |
| Safety and refusals | Unsafe content, or an over-refusal that sounds polite | Label cases where a refusal is correct and cases where an answer is required |
| Task completion | Text that reads well while the workflow step never happened | Verify the resulting state, such as the ticket updated or the tool called with the right arguments |
Once those dimensions are visible, the team needs a decision rule tied to its use case: which failures block a release, which are tolerable, and how severe each one is. Without that rule, a new version can win on tone and lose on the things that matter.
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Step 1: Define what good means for this task
Start with the user task, not the model. Write one or two sentences describing what a user is trying to get done and what a successful outcome looks like. Then list the ways the application can fail, and rank them by severity.
Illustration (a hypothetical workflow, not a measured result): a team builds an assistant that drafts replies to billing questions. Its success criteria might be that the reply cites the correct plan, does not invent a fee or refund amount, includes the next step the customer should take, and follows the required tone. A wrong refund amount is a blocking failure. An awkward greeting is a minor one. Recording that difference up front keeps later scoring from treating every flaw as equal.
- User task: the job the output has to do, in the user’s terms.
- Success criteria: observable conditions a reviewer or check can confirm.
- Failure modes: specific ways the output can miss, each with a severity level.
- Out-of-scope behavior: what the application should decline or hand off.
Step 2: Build a representative set of cases
An eval is only as informative as its cases. The OpenAI evaluation guidance pairs production data with examples written by domain experts, and that combination is a sensible default. Production examples show how real users phrase requests, including typos, partial context, and contradictory instructions. Expert-written cases cover rare but costly situations that have not yet appeared in logs.
Include edge cases on purpose. A dataset of easy, typical requests will report high scores that say little about the situations where the application is most likely to fail.
Several practical points follow:
- Record where each case came from, such as production log, expert, or failure report.
- Tag cases by slice (for example, by product line, language, or request type) so results can be read per slice, not only in aggregate.
- Version the dataset. A score is meaningful only alongside the dataset version that produced it.
- Keep a held-out portion that you do not tune against, so improvements are not measured on material you already optimized for.
None of the cited guidance gives a universal dataset size. Grow the set until each important slice has enough cases to show a difference you would act on, and state the counts whenever you report results.
Step 3: Establish a human-reviewed baseline
Subjective qualities such as helpfulness, clarity, or appropriate tone need a rubric before they can be scored consistently. A rubric defines each criterion, gives a scale, and includes an example of each level. Without anchored examples, two reviewers will use the same number for different things.
For comparisons between versions, use blinded, randomized presentation. Hide version labels, randomize which output appears first, and keep the criteria identical across both. This reduces the chance that expectations about a new version shape the judgment.
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- Write a rubric with one definition per criterion and a short example for each score level.
- Have two or more reviewers score the same sample independently.
- Compare disagreements. Decide whether the rubric is ambiguous or the case itself is unclear.
- Revise the rubric, re-score the disputed cases, and record what changed.
- Freeze the rubric at a named version and store it with every result that uses it.
Do not hide disagreement by averaging it away. Disagreements are the most useful output of this stage because they show where the criterion is underspecified.
Step 4: Automate the mechanical checks, then audit any model grader
Some properties can be tested directly. Others need a judgment, and that judgment can be delegated to a model grader. The practical rule is to use deterministic checks wherever a property can be verified mechanically and reserve model graders for semantic or subjective questions.
| Property | Suitable method | Watch for |
|---|---|---|
| Output parses as valid JSON with required fields | Schema validation | Valid structure can still contain wrong values, so pair it with content checks |
| Required phrase present, or banned term absent | String or pattern matching | Synonyms and paraphrases that a literal match misses |
| Length or item-count limit | Counting code | Limits that conflict with completeness criteria |
| Claims supported by a source document | Model grader with a written rubric, audited against human labels | Graders that agree with the output’s style rather than its accuracy |
| Helpfulness or tone | Model grader or human review, calibrated against human labels | Drift in the grader’s behavior after its own underlying model changes |
Auditing a model grader
A model grader is itself a system whose outputs need validation. The OpenAI guidance on evaluation warns against ignoring human feedback when assessing automated metrics. Once a grader exists, run it on a held-out subset that humans have already scored, measure how often the two agree, and read the disagreements one by one. If agreement is weak on a slice, the grader is not ready to decide that slice.
Graders are valuable because they run at scale. That benefit only holds after the audit, and the audit should be repeated when the grader or the application changes.
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Step 5: Turn failures and agent traces into repeatable cases
The fastest way to improve a dataset is to convert real failures into test cases. For a single-turn application, a failure is an output that missed a criterion. For an agent, the failure often sits in an intermediate step: a wrong tool call, a skipped lookup, or a handoff that dropped context. OpenAI’s guidance on agent workflows recommends inspecting traces first to understand behavior, then building datasets and evaluation runs from what the traces reveal.
- Capture the trace for each failing run, including tool calls, arguments, and intermediate outputs.
- Classify the failure by stage, such as retrieval, tool selection, argument construction, or final answer.
- Write the case with an expected outcome that a check or grader can confirm.
- Add the case to the versioned dataset under the slice it belongs to.
- Re-run the evaluation so the new case becomes part of every future comparison.
A case is only useful if its expected outcome can be checked. “The assistant should be better at refunds” is a goal, not a test. “The assistant calls the refund-policy lookup before stating an amount” is a test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 6: Run the same evaluation before and after every change
A change can be a new prompt, a new model version, a retrieval update, or a revised tool definition. Each one should go through the same procedure: identical cases, identical criteria, identical rubric version, and a stored result. Repeated runs matter too, because outputs vary between runs. OpenAI’s guidance on evaluation runs treats repetition as part of the workflow rather than an optional extra.
When comparing two prompts, models, or application versions, hold the cases and criteria constant and report the axes that match the decision:
| Comparison axis | How to measure | How to read it |
|---|---|---|
| Task success and error severity | Pass rate on blocking criteria, plus counts by severity level | A lower overall error count can still hide a rise in the most severe errors |
| Instruction following and completeness | Mechanical checks plus per-case checklists | Check which required part was missed, not only whether one was |
| Factuality or groundedness | Claim-by-claim comparison to source material, or an audited grader | Only meaningful where the application makes factual claims |
| Safety and refusal behavior | Labeled should-refuse and should-answer cases | Measure both directions, since over-refusal is also a failure |
| Human preference or rubric score | Blinded, randomized reviewer scores on the frozen rubric | Report reviewer agreement next to the score |
| Important slices and edge cases | Scores per tagged slice | A gain in one slice can coexist with a regression in another |
| Cost and latency | Measured per run under the production configuration | Operational decision inputs, not evidence of output quality |
These axes are a starting point. The cited guidance describes the general need for criteria, task data, human calibration, repeated runs, and uncertainty; it does not prescribe one universal metric set. Choose the axes the application’s users would notice.
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Reading the scores without overclaiming
Every score carries uncertainty. A difference of a few points on a small dataset may be noise, and treating it as proof of improvement is one of the most common errors in comparing versions. Anthropic’s guidance on statistical approaches to model evaluation recommends reporting the standard error of the mean (SEM) alongside eval scores. SEM estimates how precisely the average was measured from the cases you ran. It tends to shrink as the case count grows, and it is the basis for judging whether a gap between two scores is larger than the measurement noise around them.
Read results with a few constraints in mind:
- A score is evidence about the cases you ran, not a general ranking of model quality.
- A public benchmark result does not substitute for a task-specific evaluation of your application, because the benchmark’s cases and criteria were not written for your users.
- An aggregate can improve while an important slice gets worse. Check slices before declaring a win.
- A difference you cannot distinguish from the uncertainty is a reason to collect more cases or sharpen the criterion, not a reason to ship.
Report the context with every result: the dataset version and case count, the slice breakdown, the rubric version, the grader audit status, the SEM, the reviewer agreement where humans scored, and the date and configuration of the run.
What the evidence does and does not establish
Evaluation is an ongoing measurement practice rather than a one-time benchmark victory. NIST describes AI measurement and evaluation as an active area that spans metrics, methods, and standards work. Its program announcements, including the NIST GenAI Challenge on April 29, 2024 and the Assessing Risks and Impacts of AI (ARIA) program on July 26, 2024, are dated facts about those programs. They are not performance results for any particular model or application.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The OpenAI and Anthropic guidance cited here describes concepts such as datasets, graders, traces, evaluation runs, and confidence measures. It does not require a particular tool, and it does not promise that any specific workflow will improve an application. Vendor documentation and platform interfaces change, so check current pages before copying menu paths or settings into your own process.
Make the eval part of the release loop
Keep the dataset versioned, and make failure review a standing step in each release cycle. Read the failures, not only the scores, and convert the instructive ones into cases. When the product or the user task changes, revise the success criteria and the tests that depend on them, and record which version of the eval each result came from. The goal is not a perfect score. It is a process that tells you, before users do, whether the change you just made helped the task you actually care about.
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