Turn traces into a training dataset by defining the behavior you want to teach, selecting and reviewing relevant trace records, adding correct targets, protecting sensitive data, mapping records to the chosen training format, and validating the result. A trace is raw evidence of what happened—not automatically a good training example. Keep evaluation data separate from training data so you can test whether a change actually helps.
Decide what the dataset is for
Start by writing down the behavior you want to improve or measure: for example, answering a particular class of support questions, choosing tools correctly, or following a required response format. More trace volume will not resolve an unclear goal.
Choose whether you need supervised fine-tuning examples, an evaluation or regression set, preference data, or separate datasets for different purposes. An evaluation dataset measures behavior and can be rerun against different model, prompt, or agent versions; training data supplies examples used to update a model. A production trace can seed either, but a successful training run alone does not show that the model improved. Microsoft describes reusable evaluation datasets for regression testing, CI/CD quality gates, and comparisons across evaluation runs in its evaluation dataset documentation.
Capture and select the right traces
Export records with useful context
A trace may contain multiple spans, such as a user input, model call, retrieval activity, tool call, and final response. Available fields depend on how the application is instrumented and on the trace platform. OpenTelemetry’s tracing API and SDK support instrumentation, collection, and export of telemetry; they do not provide a universal labeling or fine-tuning recipe. See OpenTelemetry .NET traces.
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Use the trace platform’s filters to select the relevant scenario, outcome, time window, or recorded attributes. MLflow documents selecting traces in its UI or querying them with the SDK. Microsoft Foundry documents selecting an agent and time range when generating a dataset. The available choices depend on what your application recorded and what the platform exposes.
Curate for signal and coverage
Exclude empty, malformed, irrelevant, or low-signal records. Deduplicate near-identical requests so one common pattern does not dominate. Include varied scenarios and meaningful failures when they match the behavior you want to teach or test. Review examples directly as well as using metadata or scores.
Microsoft Foundry documents automated trace sampling that filters low-intent traffic, uses MinHash to select diverse representative examples, and handles sensitive content including personal data. Those are capabilities of the documented Foundry workflow, not a guarantee about other platforms. MLflow offers a more hands-on curation path: filter or query traces and inspect low-quality outputs, edge cases, missing context, and faulty reasoning. Automated filtering can narrow the review set, but it cannot establish that every selected example is suitable.
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Microsoft Learn says its trace-dataset flow recommends at least 15 samples. This is a product-specific recommendation for that flow, not a universal minimum for a useful training dataset. The same documentation marks the trace-to-dataset feature as preview, notes that preview features may have constrained support, and does not recommend them for production workloads. Check its current status, supported regions, SDK version, and permissions before relying on the feature: Convert agent traces into evaluation datasets (preview).
Set correct targets and expectations
For supervised fine-tuning
Choose the response or behavior the model should learn. Do not automatically copy the production response: if it is wrong, incomplete, or unsafe, using it as a target can teach the wrong behavior. Correct it, annotate it, or leave the trace out.
For evaluation
Define what success means and attach an expected answer, required facts, constraints, tool-use requirements, or a rubric appropriate to the task. MLflow documents logging expectations on traces and adding those traces to evaluation datasets. Expectations should express the intended behavior, not merely describe what the recorded system did. See MLflow: Building Agent & LLM Evaluation Datasets.
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Protect private data and keep provenance
Inspect prompts, model outputs, retrieved content, tool arguments, and metadata for personal, confidential, or otherwise restricted information before reuse. Apply the access, minimization, and retention rules that govern your application. Where possible, keep a source trace ID or equivalent provenance field so a row can be reviewed, corrected, or removed if its origin is challenged.
Vendor features do not determine your organization’s obligations. Microsoft Foundry documents sensitive-content handling in its sampling workflow. OpenAI says API data is not used to train or improve its models unless the customer opts in; its platform documentation also describes retention and application-state behavior that varies by endpoint and settings. Check the current controls for the specific endpoint and account before sending or storing trace-derived data: OpenAI platform data controls.
Map traces to the destination format
There is no universal dataset row format. Make an explicit mapping from trace fields to the chosen destination’s schema. A conceptual record might contain conversation messages, relevant context, a desired response or evaluation expectation, scenario tags, quality labels, and source provenance; the target platform may require a different structure.
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Microsoft Foundry says evaluation datasets typically use JSONL, with a messages field for model or agent interactions. If completed responses are included, Foundry can evaluate those responses directly; when evaluating against a live model or agent, it generates a new response and evaluates that instead. See Evaluation datasets in Microsoft Foundry.
OpenAI’s fine-tuning API requires a JSONL training file, but its contents vary according to whether the target uses chat, completions, or preference format. Do not assume raw traces can be uploaded as-is; transform and validate them for the selected fine-tuning method. See the OpenAI fine-tuning API reference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a trace-to-dataset workflow
| Path | What it supports | Trade-offs and checks |
|---|---|---|
| Microsoft Foundry | Select an agent and date range, generate trace-derived datasets in the portal or SDK, preview rows, and proceed to evaluation or fine-tuning. Intelligent sampling is documented. | The trace-dataset feature is marked preview; check current status, supported regions, SDK version, and permissions. Its sampling behavior is specific to this workflow. Microsoft Learn documentation. |
| MLflow | Select traces through the UI or SDK, filter and inspect them, add expectations, and build reusable evaluation datasets. | The documentation requires an MLflow Tracking Server with a SQL backend for evaluation datasets. Expect to manage curation explicitly. MLflow documentation. |
| Custom export and transformation | Extract traces from an existing trace store or telemetry export, transform them to the destination schema, and validate them with the destination provider. OpenTelemetry supplies instrumentation and export primitives; OpenAI documents JSONL training-file requirements for its fine-tuning API. | You own filtering, deduplication, privacy handling, labels, provenance, schema changes, and validation. OpenTelemetry and OpenAI fine-tuning API. |
Compare workflows by trace-selection and export control, labeling support, schema flexibility, provenance and versioning, privacy and retention controls, model compatibility, operational maturity, and the amount of custom pipeline work. Documentation alone does not establish that one path produces better data or model results for your task.
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Validate and version the dataset
Before training or evaluation, inspect a sample of rows and check the complete dataset for:
- Valid parsing and required fields for the destination schema.
- Correct turn ordering and consistent representation of tool calls.
- Nonempty, appropriate targets or expectations.
- Uncontrolled duplicates, irrelevant records, or missing scenario coverage.
- Sensitive fields that should be removed, masked, or access-controlled.
Record the dataset version, source time window, transformation code or version, filtering criteria, and label provenance. Foundry lets users preview generated rows, download them, or delete the dataset. MLflow supports reusable datasets, expectations, and source-type provenance. These features make curation more reviewable, but they do not guarantee that examples or labels are correct.
Keep a held-out evaluation set separate from examples used for training wherever possible. Run the model or agent against that set and examine both aggregate measures and individual failures. If behavior regresses, use provenance to locate the relevant source examples and adjust the data or transformation pipeline.
When production traces are not enough
Production traces show behavior in scenarios users have actually encountered, but they cannot by themselves cover every prelaunch scenario or rare edge case. Microsoft Learn describes trace-based and synthetic generation as complementary: production traces reflect real user behavior, while synthetic generation can cover prelaunch scenarios and edge cases. Add carefully designed scenarios for important gaps rather than treating observed traffic as complete coverage. See Microsoft’s trace-dataset guidance.
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