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| last-modified | Wed, 30 Sep 2026 01:28:40 GMT |
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@@ -152,11 +152,17 @@ Every link points to the markdown version of a page (same URL with a `.md` exten- [Datasets](https://docs.mistral.ai/studio/observability/evaluations/datasets.md): A dataset is the set of test cases that drives an offline evaluation.- [Evaluators](https://docs.mistral.ai/studio/observability/evaluations/evaluators.md): An evaluator scores each record of a run.- [Set goals](https://docs.mistral.ai/studio/observability/evaluations/goals.md): Goals let you define pass/fail criteria on evaluator scores.-
- [Migration guide and changelog](https://docs.mistral.ai/studio/observability/evaluations/migrating.md): This page lists changes per version — breaking changes (with exactly what to update) and notable additions.- [Optimize prompts and parameters](https://docs.mistral.ai/studio/observability/evaluations/optimization.md): Optimization turns an evaluation into a search: instead of measuring a single prompt or set of system params, the SDK automatically explores variations of it and returns the best one it fin…- [Offline evaluations](https://docs.mistral.ai/studio/observability/evaluations.md): The Evaluation SDK (mistralai-evaluations) lets you run offline evaluations on your LLM pipelines in a few lines of Python.-
- [Configure statistics](https://docs.mistral.ai/studio/observability/evaluations/statistics.md): Statistics are the run-level values an evaluator exposes from its per-record numeric scores: an average, a total, a percentile.+
- [Configure statistics](https://docs.mistral.ai/studio/observability/evaluations/statistics.md): Statistics are the run-level values an evaluator exposes from its per-record scores: an average, a total, a percentile, or a classification metric such as F1.- [Configure system params](https://docs.mistral.ai/studio/observability/evaluations/system-params.md): When you run an LLM pipeline, many parameters influence the output: the model, temperature, system prompt, tool definitions, retrieval settings, and more.+
- [Workflow evaluation plugin API reference](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin/api-reference.md): Reference for evaluation.run(), its result, and the plugin's imports.+
- [Building blocks](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin/building-blocks.md): When your workflow already manages its own fan-out, for example with custom child workflow orchestration, call the steps of evaluation.run() yourself instead.+
- [Decorators](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin/decorators.md): Every function you pass to the plugin runs as a Temporal activity.+
- [Optimize prompts and parameters in a workflow](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin/optimization.md): evaluation.optimize() runs the optimizer inside a workflow.+
- [Workflow evaluation plugin](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin.md): The Workflow evaluation plugin (mistralai-workflows-plugins-evaluations) runs offline evaluations inside Mistral Workflows.+
- [Rescore persisted runs in a workflow](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin/rescoring.md): evaluation.rescore() scores the outputs already persisted in Studio without re-running the task, with your scorers running as activities.+
- [Task modes](https://docs.mistral.ai/studio/observability/evaluations/workflows-plugin/task-modes.md): The task parameter of evaluation.run() accepts three kinds of values.- [Observability](https://docs.mistral.ai/studio/observability.md): Observability is in Private Preview and is available for Enterprise-tier organizations only.- [Data redaction](https://docs.mistral.ai/studio/observability/traces/data-redaction.md): Spans capture the input and output of each operation, including prompts, responses, and tool call arguments and results.- [Explore traces](https://docs.mistral.ai/studio/observability/traces/explorer.md): The Trace Explorer is where you search, filter, and inspect every trace flowing through your AI applications.@
@@ -242,6 +248,7 @@ Every link points to the markdown version of a page (same URL with a `.md` exten- [API Error Codes](https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/error_codes.md): When a request to the Workflows API fails, the response includes a structured error code in WF_XXXX format:- [Execution Context](https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/execution_context.md): Access runtime information about the current workflow execution from within workflow code.- [Hardened deployments](https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/hardened_deployments.md): A hardened deployment is a standard deployment with restricted workflow registration.+
- [Managed deployments](https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/managed-deployments.md): Deploy a workflow worker from a GitHub repository.- [Rate Limiting](https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/rate_limiting.md): Rate limiting is a crucial aspect of workflow management that helps control resource consumption and prevent any single workflow or activity from monopolizing shared resources.- [Resetting Workflows](https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/reset_workflow.md): Reset a workflow execution to restart it from a specific point in its event history.- [Observability](https://docs.mistral.ai/studio/workflows/observability.md): This guide covers how worker telemetry (logs, traces, and metrics) is exported, and how to use OpenTelemetry traces for execution-level diagnostics.1 line shown here cut at 300 characters. The raw artifact at this commit is linked above.