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A reproducible AI evaluation is more than a score. It is a score linked to a versioned evaluation set, an explicit prediction and scoring procedure, the exact model or application configuration, and a retained run record. Build those four pieces first, then compare versions on the same data, inspect individual failures, and publish enough context for another engineer to understand or rerun the result.
What makes an AI evaluation reproducible?
An evaluation is reproducible when another person—or you six months later—can identify exactly what was tested, how outputs were produced, how they were judged, and which software and configuration generated the result. The practical unit is therefore not “Model A scored 82%.” It is:
- Evaluation data: the named dataset revision and the examples included in the run.
- Prediction procedure: the application code, prompt or system instructions, model/provider version, and generation settings that produced each output.
- Scoring procedure: metric definitions, thresholds, custom checks, evaluator versions, and any human or model-judged criteria.
- Run context: code revision, dependency and runtime details, timestamps, outputs, traces, and artifacts.
A score without those attachments cannot tell you whether a change improved the system, whether a different sample was used, or whether the scorer itself changed. Reproducibility also does not mean that a stochastic model will emit byte-for-byte identical text forever. It means that the sources of variation are controlled, recorded, and measurable.
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- Define the deployment decision, intended use, users, and failure consequences.
- Create a versioned evaluation set containing ordinary, difficult, safety, domain, and previously failed cases.
- Make the prediction interface and scoring procedure explicit.
- Record the complete run context and retain per-example artifacts.
- Compare versions with the same data and scoring process, changing one major factor at a time where practical.
- Inspect failures, changed examples, rationales, and traces instead of relying only on averages.
- Package the data, code, configuration, results, and limitations in a portable report.
The order matters. If you select metrics before deciding what the system is supposed to do, or compare versions after silently changing the test set, the resulting number can be precise but irrelevant.
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1. Define the decision and intended use before choosing metrics
Write a one-paragraph evaluation charter before collecting examples. State what the model or application does, who will use it, where it will be used, and what a failure means. A customer-support assistant, a document classifier, and a code-generation tool need different test cases and different definitions of harm.
Describe the decision the evaluation must support
Use a decision statement such as: “Approve release candidate B for internal support agents if it preserves answer accuracy on the baseline set, does not worsen refusal behavior on prohibited requests, and stays within the measured latency budget.” This makes the evaluation a release decision rather than a leaderboard exercise.
Turn intended use into failure categories
| Question | What to specify | Why it changes the evaluation |
|---|---|---|
| Who uses the system? | Roles, expertise, language, accessibility needs, and supervision | The same error may be recoverable for an expert and dangerous for an unsupervised user. |
| What inputs are in scope? | Formats, languages, domains, length limits, and expected traffic | Out-of-scope inputs should be tested for safe handling, not silently mixed with normal accuracy. |
| What is a failure? | Wrong facts, missing required fields, unsafe advice, leakage, unsupported claims, or unusable latency | Each failure category needs an observable check or an explicit judgment rule. |
| What is the release threshold? | Required minimums, maximum allowed regressions, and escalation rules | A mean score alone cannot encode a zero-tolerance safety failure. |
Do not present a benchmark number as a complete statement of fitness for use. A result is valid only for the conditions, populations, input types, and failure definitions you measured.
2. Build a versioned evaluation set
Create a stable baseline set and assign every revision an immutable identifier. The set can begin with manually written examples, production traces that have been scrubbed and approved for evaluation, or both. MLflow’s evaluation-dataset guidance describes these datasets as useful for preventing regressions, comparing versions on the same data, targeting specialized issues, and validating behavior across environments. See MLflow’s evaluation dataset documentation.
Include the cases that matter, not only the average case
| Case group | Purpose | Typical contents |
|---|---|---|
| Representative ordinary cases | Measure normal task quality | Inputs sampled from the intended workload, with sensitive data removed or protected. |
| Edge and boundary cases | Expose behavior near limits | Long inputs, ambiguous wording, empty fields, unusual formats, multilingual or low-quality text. |
| Safety and abuse cases | Check refusal, safe completion, privacy, and security behavior | Requests that should be declined, redirected, or handled with a warning. |
| Domain-specific cases | Test terminology and rules that generic benchmarks miss | Organization policies, regulated workflows, product-specific entities, or specialist labels. |
| Known failures | Prevent a fixed bug from returning | Every incident or manually confirmed regression, with its original context and expected correction. |
Give every sample a stable identity and expectation
A useful record has an immutable sample ID, input, expected result or judging criteria, category or slice labels, source and provenance, and the dataset revision in which it first appeared. When a single answer is objectively correct, store a ground-truth expectation. When several answers can be acceptable, write explicit criteria instead of pretending that one reference string is the only truth.
The current MLflow quickstart summarizes its format as “The evaluation dataset is a list of samples, each with an inputs and expectations field.” Its example and evaluation call are documented at the MLflow Evaluation Quickstart. You can use a different storage format, but preserve the same semantic fields.
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Version additions and edits separately
- Never overwrite a baseline revision used in a published run.
- Record whether a sample was added, edited, relabeled, or retired.
- Keep a changelog explaining why the set changed and which release first used the new revision.
- Use the same revision for both candidates in a comparison unless the explicit question is the effect of changing the dataset.
- Track slices such as language, product area, input length, risk class, or demographic group when they are relevant and supported by the data.
Do not invent a universal sample count. The right size depends on the decision, the diversity of the intended workload, the rarity and severity of failures, and the uncertainty you can tolerate. Increase coverage where an error would be costly, not merely where it is easy to collect examples.
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Write down the interface that turns one evaluation record into a prediction. For an application, that may include retrieval, tool calls, prompt assembly, post-processing, and the model call—not just the underlying model name. A scorer then turns the prediction and expectation into a metric, a pass/fail result, a rationale, or a structured error label.
Use deterministic checks wherever the property is directly testable
- Exact or normalized match for controlled labels and identifiers.
- Schema validation for required fields, data types, and allowed values.
- Programmatic tests for citations, JSON validity, forbidden strings, numerical ranges, or policy rules.
- Task-specific calculations such as precision, recall, ranking measures, or edit distance when their assumptions fit the task.
Deterministic checks are easier to audit and rerun. Document normalization rules, case handling, tokenization, tolerances, and what happens when the output is malformed.
Document subjective and model-judged criteria
For qualities such as helpfulness, completeness, tone, or factuality without a single reference answer, define a rubric with observable levels and examples. Record the evaluator model and version, its prompt, temperature or equivalent settings, and the rationale it produced. Treat model judgments as measurements requiring validation: review a sample of rationales, compare them with human decisions where possible, and monitor disagreement rather than presenting the judge as ground truth.
Control stochastic behavior without hiding it
Pin generation parameters, seeds when the provider supports them, stop conditions, tool versions, and concurrency settings. If the system or scorer remains stochastic, run repeated trials and report the distribution or range, not only one run. Keep the repetition count and aggregation rule in the run record. A deterministic seed does not guarantee identical results across provider model revisions, hardware, or dependency changes, so those details must also be recorded.
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Create a machine-readable manifest for every run. The following fields cover the information most often needed to explain a regression:
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| Manifest area | Record |
|---|---|
| Identity | Run ID, UTC start and end times, operator or pipeline, and parent comparison or release ID. |
| Code | Repository commit, uncommitted-change status, evaluation script version, and application build. |
| Model | Provider, model name and version or snapshot identifier where available, endpoint or deployment region when relevant. |
| Instructions | System and developer prompts, templates, tool definitions, retrieval configuration, and post-processing rules. |
| Generation | Temperature or equivalent sampling settings, top-p or other controls, maximum output length, seed, stop conditions, and retry policy. |
| Dataset | Dataset name, immutable revision, sample IDs, slice filters, and any excluded records with reasons. |
| Scoring | Metric and rubric definitions, scorer code revision, evaluator model version, thresholds, normalization, and aggregation formula. |
| Environment | Language and runtime versions, dependency lockfile or image digest, hardware, relevant feature flags, and network or service configuration. |
| Artifacts | Raw and processed outputs, per-example scores, rationales, traces, logs, error reports, and the final summary. |
MLflow Tracking describes logging parameters, code versions, metrics, and artifacts, while its evaluation documentation associates datasets with experiments. Those concepts are useful even if you implement the record in another system; see MLflow’s evaluation-driven workflow documentation.
Protect sensitive evaluation data
Store only the fields needed to evaluate the intended behavior. Redact personal or confidential information, restrict access to raw outputs and traces, and keep a documented mapping if an authorized reviewer must reconnect a sample to its source. Hashes can identify an artifact without exposing its contents, but a hash alone is not a substitute for retaining the data under an appropriate access policy.
5. Compare versions as controlled changes
For a fair comparison, hold the dataset revision, sample order, prediction interface, scorer definitions, and aggregation rules constant. Change one major factor at a time where practical: model version, prompt, retrieval index, application code, or generation settings. If several factors must change together, label the result as a bundle and do not attribute the entire difference to one component.
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| Comparison dimension | What to report | Common mistake |
|---|---|---|
| Task quality | Overall metric plus error categories and important slices | Reporting only a mean that hides a severe class-specific regression. |
| Safety and robustness | Pass rates and concrete failures on relevant edge, abuse, and adversarial cases | Assuming high ordinary accuracy proves safe behavior. |
| Population or context | Performance by relevant language, demographic, domain, or input type when the data supports it | Making group claims from too few or nonrepresentative examples. |
| Latency | Measured timing definition, percentile or distribution, hardware and service conditions | Comparing a warm-cache run with a cold-cache run without disclosure. |
| Token usage | Input and output usage captured from the trace, with the counting method | Inferring usage from character counts or an unrecorded estimate. |
| Cost | Actual measured spend, pricing date and region, and included services | Adding a theoretical price when no cost was measured. |
| Variability | Repeated-run results, spread, and stochastic settings | Treating one random draw as a stable improvement. |
Use paired per-example analysis when possible: identify which records changed from pass to fail, fail to pass, or one error category to another. A small aggregate improvement may be unacceptable if it comes from sacrificing a high-risk slice.
6. Inspect examples, rationales, and traces
After calculating aggregates, open the evidence behind them. Review all failures in a small critical set and a stratified sample of other failures. Examine examples that changed between candidates, not only examples that failed in both. Look for systematic issues such as truncation, retrieval misses, instruction conflicts, malformed structured output, or a judge rubric that rewards superficial wording.
Use traces to connect an outcome to a cause
For multi-step applications, retain the input, retrieved context, tool calls, intermediate decisions, final output, timing, and token usage where governance permits. A trace can show whether a low score came from the model, a missing document, a tool timeout, a parser, or a post-processing rule. Keep scorer rationales alongside the prediction so a reviewer can challenge the judgment rather than seeing only a number.
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Analyze slices before approving a release
- Compare each important category with the baseline, not just the global average.
- Check the count behind every percentage; a high rate from a handful of samples is uncertain.
- Review new failures manually and classify their root cause.
- Promote confirmed failures into a future dataset revision, with an explanation of the fix they are intended to guard.
- Record unresolved disagreements instead of silently forcing a label.
The MLflow quickstart demonstrates detailed scorer metrics, rationale inspection, and prediction-trace inspection; its current procedure is documented at the quickstart page.
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A colleague should be able to understand the result without access to your terminal or an undocumented dashboard. Publish or archive a report containing:
- Intended use, out-of-scope use, and the decision the run informs.
- Model or application identity, code revision, prompts, configuration, and runtime details.
- Dataset revision, inclusion and exclusion rules, slice definitions, and provenance.
- Scorer definitions, evaluator configuration, thresholds, aggregation, and repeated-run policy.
- Overall and slice-level results, uncertainty or run-to-run variation where measured, and operational observations.
- Representative failures, known limitations, unresolved questions, and links or identifiers for retained artifacts.
This is consistent with the reporting principle in Model Cards for Model Reporting: performance should be documented in relevant conditions together with intended use and evaluation procedures. The paper defines model cards as “short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains.” Apply that principle only to groups and conditions your data can support; do not imply coverage you did not measure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical repository and manifest layout
Keep evaluation assets separate from transient logs and make immutable revisions easy to identify. One workable layout is:
eval/
datasets/
support-baseline-v3.jsonl
CHANGELOG.md
scorers/
answer_schema.py
safety_rubric_v2.md
runners/
evaluate.py
manifests/
2026-10-13_candidate-b.json
results/
2026-10-13_candidate-b/
per_example.jsonl
summary.json
traces/
reports/
2026-10-13_candidate-b.htmlThe directory names are illustrative; the important property is that a result points to immutable versions rather than “latest” files. A manifest can be JSON, YAML, or a database row. For example:
{
"run_id": "2026-10-13-candidate-b",
"dataset": {"name": "support-baseline", "revision": "v3"},
"application_commit": "9f2c1d7",
"model": {"provider": "recorded-provider", "name": "recorded-model", "version": "recorded-version"},
"generation": {"temperature": 0, "seed": 17},
"scorer": {"name": "support-rubric", "revision": "v2"},
"environment": {"python": "3.10.x", "lockfile_hash": "recorded-hash"}
}Replace illustrative values with the actual identifiers generated by your pipeline. Do not use a mutable alias such as “latest” as the only reference.
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Using MLflow or another evaluation platform
A tracking platform can centralize runs, parameters, metrics, artifacts, datasets, and traces. MLflow’s current quickstart shows the pattern of a prediction function, an evaluation dataset with inputs and expectations, built-in or custom scorers, and an evaluation call. Its APIs and package requirements can change, so pin dependencies in a real project and check the current documentation before copying an example.
There is an important product-specific constraint: the current MLflow evaluation-dataset feature requires a Tracking Server backed by SQL—PostgreSQL, MySQL, SQLite, or MSSQL—and is not available in FileStore. That is a requirement for this MLflow feature, not for reproducible evaluation in general. A self-managed repository, database, or another tracking system can implement the same evidence model if it preserves immutable dataset identity, run context, and artifacts.
MLflow is not the only approach. The Evaluate and Evaluation on the Hub paper describes a software library aimed at reproducibility, centralized documentation, and broader measurement coverage. It reported more than 50 efficient canonical metric implementations and, at publication in 2022, more than 75,000 models and 11,000 datasets on its evaluation hub. Those are historical figures from the paper, not current platform counts, and should not be used as a present-day inventory.
Choosing tooling without losing reproducibility
| Approach | Advantages | Questions to resolve |
|---|---|---|
| Version-controlled files and scripts | Transparent, portable, inexpensive, and easy to review in code changes. | How will you index runs, store large traces, enforce access controls, and compare results across teams? |
| Self-managed tracking server | Centralized metadata and artifacts with control over hosting and data governance. | Who operates the database, backups, retention, authentication, and upgrades? |
| Hosted evaluation platform | Faster collaboration, dashboards, trace search, and managed storage. | Where does sensitive data go, how are versions exported, what are the recurring costs, and can you rerun outside the service? |
| Metric library plus your existing tracker | Reusable scorers while keeping experiments in current infrastructure. | Are metric assumptions, versions, and evaluator prompts captured with every run? |
Choose based on data governance, existing infrastructure, scale, collaboration needs, and total operating cost. A sophisticated dashboard cannot compensate for an unversioned dataset or an undocumented scorer.
Common failure modes and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| The score changed after a harmless code edit. | Prompt assembly, dependency, model alias, or preprocessing changed unnoticed. | Pin versions, store the full prompt and configuration, and compare per-example outputs. |
| Two teams report different scores for the same model. | Different dataset revisions, filters, normalization, or scorer versions. | Compare immutable dataset and scorer IDs, then rerun both with one shared manifest. |
| The average improved but users report worse behavior. | A critical slice or failure category is diluted by easy examples. | Inspect slice-level metrics and changed failures; add the incident to the regression set. |
| Model-judge results look unstable. | Unpinned evaluator model, rubric, sampling, or provider revision. | Record evaluator details, reduce unnecessary randomness, repeat trials, and spot-check rationales. |
| A run cannot be reconstructed. | Only aggregate metrics were retained, or logs refer to mutable “latest” assets. | Archive per-example outputs, traces, manifests, code revisions, and immutable dataset artifacts. |
| The tracking system cannot create evaluation datasets. | For current MLflow evaluation datasets, the tracking server uses FileStore instead of SQL. | Configure a supported SQL-backed Tracking Server or use another storage design; do not treat this product constraint as a universal rule. |
| Latency or cost claims cannot be reproduced. | Timing conditions, token accounting, region, or pricing date were omitted. | Record measurement boundaries, warm-up state, token source, provider conditions, and the date of any price calculation. |
Release checklist
- Decision: intended use, users, in-scope inputs, failure definitions, and release thresholds are written.
- Data: every sample has a stable ID; the dataset revision, provenance, labels, and slices are recorded.
- Prediction: model identity, prompts, retrieval, tools, generation settings, retries, and post-processing are pinned or logged.
- Scoring: deterministic checks, rubrics, evaluator versions, rationales, and aggregation rules are versioned.
- Comparison: candidates used the same data and scoring process, with major changes isolated where practical.
- Evidence: per-example results, failures, changed cases, and relevant traces are retained.
- Reporting: performance, conditions, intended use, limitations, and operational measurements are documented.
- Portability: a colleague can obtain the code, data revision, manifest, environment information, and artifacts needed to rerun or audit the evaluation.
Once this process is in place, a model change becomes a traceable experiment rather than an argument over a single benchmark number. The workflow can start with files and scripts, then adopt a tracking system as run volume and collaboration grow; the reproducibility requirements remain the same.
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