# Weights Training
Generated: 2026-11-02T05:42:09.320Z
Source build: local
Canonical docs: https://teammately.ai/docs
---
id: assets.weights
title: Weights Training
summary: Prepare benchmark-guided training data, inspect coverage and splits, download exact packages, and follow training and evaluation results.
kind: reference
product_area: weights_training
status: stable
updated: 2026-10-30
canonical: /docs/weights-training
---
# Weights Training
Weights training turns selected Benchmark criteria into inspectable training material and connects that material to model training. Open it from the Benchmark workspace.
## Definition
A preparation records the Benchmark Version, training method, selected scope and generation settings. The workspace has **Overview**, **Dataset**, **Coverage** and **Results**.
Overview summarizes the preparation and prepares the exact dataset download. Dataset exposes examples and splits. Coverage shows representation. Results configures and follows connected training and separately authorized evaluation.
## Fields, states, or lifecycle rules
- Demonstration and preference datasets have different learner targets.
- Examples retain their governing criteria and source context.
- Training, validation and test splits remain distinguishable.
- A download package captures an exact dataset state and export profile.
- A training run records the connection, base model, configuration, provider job and resulting model identity.
- Training completion does not establish benchmark improvement.
## Use the workspace
1. Create a preparation for the intended Benchmark Version and method.
2. Inspect generated examples in Dataset.
3. Check splits and meaningful representation in Coverage.
4. Use **Prepare download** in Overview and inspect the package settings.
5. Download the exact ready package or use Results to configure a connected training service.
6. Launch training deliberately and follow the recorded job.
7. Authorize evaluation separately and inspect the actual resulting responses and judgments.
A provider connection is not required to download a canonical dataset. Connected training services accept only the methods, model families and material types supported by their selected profile.
## Keep training and evaluation separate
Generation checks describe prepared material. Coverage describes its representation. A provider completion identifies a training output. Held-out evaluation measures that output through its supported inference path. None substitutes for the others.
Keep test records out of training and provider validation uploads. Preserve exact package and model identities when comparing results with an ordinary Harness evaluation.
## Related task pages
{% related-card-grid title="Related task pages" %}
- [Training Datasets and Coverage](/docs/weights-training/datasets)
- [Export, Train, and Evaluate](/docs/weights-training/exports-and-training)
- [Workspace Integrations](/docs/admin-console/integrations)
- [Evaluation Results](/docs/benchmark-evaluations/results)
{% /related-card-grid %}
---
id: weights-training.datasets
title: Training Datasets and Coverage
summary: Inspect generated training examples, preserve related families across splits, and assess representation from captured project facets.
kind: task
product_area: weights_training
status: stable
updated: 2026-10-30
canonical: /docs/weights-training/datasets
---
# Training Datasets and Coverage
Inspect the dataset before exporting or training. Generation completion does not establish realistic examples, balanced coverage or useful learned behavior.
## Prerequisites
- A preparation tied to the intended Benchmark Version.
- Generated examples or a supported existing dataset.
- Clear training purpose and held-out evaluation intent.
## Inspect Dataset
Open Dataset and review the input, response or preference target, captured criteria and observed judgments. Expected labels are not substitutes for the actual outcomes.
Keep related construction-family members in the same split. A small input variation must not leak the same underlying situation across training and held-out testing. Inspect train, validation and test populations before packaging.
Exclusion and split changes produce a new dataset state. Existing packages remain bound to their earlier captured content rather than silently acquiring those edits.
## Inspect Coverage
Coverage uses the dataset's selected state and split filter. Read classified, no-match and unavailable outcomes distinctly. Unknown or unclassified examples remain in the denominator rather than disappearing from the population.
New synthesis captures the project Dimension ontology, active Topics, Topic Groups and Scenarios used for classification. Retries reuse that vocabulary. A missing vocabulary is not a successful coverage assessment.
Comparing with the captured benchmark can reveal a thin category, but the benchmark distribution is not silently imposed as the training distribution target.
## Object and state changes
Dataset edits change current membership or split assignment through their owning commands. Representation analysis can publish a new dataset state without regenerating the input or response. It preserves recorded splits and captured vocabulary.
## Success criteria
- Examples are suitable for the selected method and purpose.
- Related families do not cross protected split boundaries.
- Coverage counts include the unclassified population.
- The exact dataset state used by a package is identifiable.
## Common failure modes
- Treating generation status as a quality score.
- Substituting expected judgments for observed results.
- Reading missing classification as proven absence.
- Training on the held-out test split.
- Assuming a package changes after the dataset is edited.
## Related reference pages
{% related-card-grid title="Related reference pages" %}
- [Weights Training](/docs/weights-training)
- [Export, Train, and Evaluate](/docs/weights-training/exports-and-training)
- [Dataset Representation](/docs/benchmark-datasets/representation)
{% /related-card-grid %}
## Related troubleshooting pages
{% related-card-grid title="Related troubleshooting pages" %}
- [Synthetic Cases That Feel Unrealistic](/docs/troubleshooting/unrealistic-synthetic-cases)
- [Unbalanced Coverage](/docs/troubleshooting/unbalanced-coverage)
{% /related-card-grid %}
---
id: weights-training.exports-training
title: Export, Train, and Evaluate
summary: Prepare an exact training package, launch an authorized provider job, and inspect a separately authorized held-out evaluation.
kind: task
product_area: weights_training
status: stable
updated: 2026-10-30
canonical: /docs/weights-training/exports-and-training
---
# Export, Train, and Evaluate
Use Overview to prepare a dataset download and Results to configure connected training and evaluation. Downloading a package does not submit a training job.
## Prerequisites
- A reviewed dataset with the intended splits.
- An export profile compatible with its method.
- An active provider connection for connected training.
- An available supported model and explicit authorization to launch.
## Prepare the package
Choose **Prepare download** in Overview. Inspect the pinned dataset, profile, counts and exclusions before creating the package. A ready package provides the canonical files and manifest for that exact dataset state.
Canonical demonstration and preference exports preserve their method-specific targets. The package can include train, validation and test files where those splits have members, plus its manifest and supporting instructions/materials. A provider-specific conversion is a separate compatibility boundary.
## Train deliberately
In Results choose the provider connection, base model and supported settings. Saving configuration alone does not upload data or start a job. Launch explicitly and follow the provider job identity and state.
Provider profiles govern supported text, materials, tools, trajectories or preference formats. An unsupported feature is rejected rather than silently removed. A successful connection probe does not establish create permission, quota or successful training.
The training transfer uses train and validation as supported. Test content remains held out. Do not upload benchmark Cases or protected rubric/construction context as learner data.
## Evaluate separately
Authorize evaluation through the supported inference connection or provider path. Training does not automatically provision an inference deployment for every service. Where the deployment association is user-reported, keep that distinction visible.
Inspect actual responses, rubric outcomes, completeness and the resulting model identity. A job that completed without a confirmed model output is not a verified trained candidate.
## Object and state changes
Package creation captures the selected dataset. Launch records an authorized training operation and provider job. Cancellation and evaluation have separate operations. Existing packages and accepted results remain attributable.
## Success criteria
- Package, dataset and provider job identities agree.
- Held-out data stays out of training.
- The resulting output is identified before evaluation.
- Evaluation uses actual behavior rather than job status.
## Common failure modes
- Assuming Save starts training.
- Dropping unsupported fields to fit a provider.
- Treating training completion as correctness.
- Claiming inference resources were provisioned merely because training succeeded.
## Related reference pages
{% related-card-grid title="Related reference pages" %}
- [Weights Training](/docs/weights-training)
- [Training Datasets and Coverage](/docs/weights-training/datasets)
- [Workspace Integrations](/docs/admin-console/integrations)
- [Inspect Evaluation Results](/docs/benchmark-evaluations/inspect-results)
{% /related-card-grid %}
## Related troubleshooting pages
{% related-card-grid title="Related troubleshooting pages" %}
- [Missing Outputs](/docs/troubleshooting/missing-outputs)
- [Benchmark run troubleshooting](/docs/troubleshooting/benchmark-runs)
{% /related-card-grid %}
---
id: admin-console.integrations
title: Workspace Integrations
summary: Connect messaging, repository, trace-store, and training services through their own authorization and operating controls.
kind: reference
product_area: admin_console
status: stable
updated: 2026-10-30
canonical: /docs/admin-console/integrations
---
# Workspace Integrations
## Definition
Open **Admin → Integrations** to connect workspace services. Each connection supplies a specific workflow rather than a general permission to read or write every external resource.
## Fields, states, or lifecycle rules
- Messaging connections support their configured notification and test operations.
- Repository connections discover repositories accessible to the authorizing account.
- Trace-store connections supply their supported capture/import workflow.
- Training connections are selected separately by a Benchmark preparation.
- Connection state, account authorization and a successful downstream operation remain distinct.
## Messaging
Slack and Microsoft Teams have provider-specific connection controls. For Teams, installation and a personal-message test are distinct operations. Workspace settings alone do not confirm installation or that automatic notifications are enabled.
Read provider errors as operational outcomes rather than treating a visible integration card as proof of delivery.
## Repositories and trace stores
GitHub.com, GitLab.com and Bitbucket Cloud connections discover the repositories allowed by the account and installation. Bind the intended repository through Reference Materials and inspect its indexed content.
LangSmith and Langfuse have their own connection surfaces. Keep original record identity and scope when importing material; connected access does not approve the captured behavior.
## Training and inference
Weights training selects an active service connection, supported model and compatible method. Connection checks do not certify quota, create-job permission, storage access or training quality.
Some evaluation paths require an existing callable inference deployment. A training job does not automatically allocate serving resources for every service. The deployment-to-output association can be user-reported where the adapter identifies it that way.
## Credentials and resource boundaries
For setup steps and provider-specific recovery, use the [integration guides](/docs/integrations#connection-setup-guides). Each guide distinguishes workspace connection, Project selection and a successful downstream operation.
Use the provider's owning connection flow and approved secret mechanism. Keep access scoped to the authorized account, identity and resource. Existing Vertex and Baseten training adapters do not authorize Google platform storage, ambient developer credentials or a provider fallback.
A failed service operation should remain actionable in its owning workflow rather than being hidden by a substitute local service.
## Related task pages
{% related-card-grid title="Related task pages" %}
- [Reference Materials](/docs/agent-setup/reference-materials)
- [Export, Train, and Evaluate](/docs/weights-training/exports-and-training)
- [Fieldloop](/docs/fieldloop)
- [Admin Console](/docs/admin-console)
{% /related-card-grid %}
---
id: benchmark-evaluations.compare
title: Evaluation Results
summary: Inspect Case and rubric outcomes and compare exact managed or imported Runs within a Benchmark Version.
kind: task
product_area: benchmark_evaluations
status: stable
updated: 2026-10-30
canonical: /docs/benchmark-evaluations/results
---
# Evaluation Results
Results presents a matrix of selected Runs and the Cases or criteria they evaluated. Use one Run for inspection or several comparable Runs to locate gains, regressions and missing judgments.
## Prerequisites
- At least one Run for the intended Benchmark Version.
- Permission to inspect its Cases, responses and judgments.
- Comparable recorded conditions when interpreting differences.
## Steps
1. Open **Benchmark Evaluations → Results** and confirm the Benchmark Version.
2. Select the visible Runs. Managed and imported Runs can both appear; an imported Run does not need a Harness Version to be inspected.
3. Choose Cases, Policies, Rubrics, Dimension ontology values, Topics, Topic Groups or Scenarios as the row grouping.
4. Read completeness before comparing scores. An empty or incomplete result is not a failed judgment.
5. Open a Case or rubric detail to inspect the response, criterion, observed judgment and rationale.
6. For repeated Trials, inspect the selected aggregation rule and the individual Trial outcomes.
7. Route an actual candidate problem to Harness improvement, a standard problem to Correctness Governance, or an input-coverage problem to Coverage Design.
## Representative and historical Runs
Default candidate results use the Run selected before its outcomes were known. **Used for score** identifies that reference. Other executions remain in Run history and can be inspected without silently replacing the representative result.
Selecting matrix columns changes the analysis view. It does not activate a Harness, execute a candidate or declare a winner.
## Object and state changes
Filtering, row grouping and inspection are read-only. An authorized project manager can separately use **Correct judgment** on an exact rubric result. That operation records a correction and preserves the original AI judgment; refreshed aggregates come from the authoritative result snapshot.
## Success criteria
- Every compared column has an identified Run and Benchmark Version.
- Differences resolve to actual Cases and applicable criteria.
- Missing outcomes stay distinct from negative scores or failed gates.
- Manual corrections remain attributable.
## Common failure modes
- Treating an imported Run as an executable Harness.
- Combining later successful Trials into an earlier reference score.
- Reading a filtered population as the entire benchmark.
- Changing a rubric judgment when the real problem is an incorrect Case or standard.
## Related reference pages
{% related-card-grid title="Related reference pages" %}
- [Inspect Evaluation Results](/docs/benchmark-evaluations/inspect-results)
- [Evaluation Dashboard](/docs/benchmark-evaluations/dashboard)
- [Rubric Points and Benchmark Scores](/docs/correctness-governance/scoring)
{% /related-card-grid %}
## Related troubleshooting pages
{% related-card-grid title="Related troubleshooting pages" %}
- [Benchmark Results Changed Unexpectedly](/docs/troubleshooting/benchmark-results-changed-unexpectedly)
- [Overlapping Rubrics](/docs/troubleshooting/overlapping-rubrics)
{% /related-card-grid %}