TensorAPI.comEvaluate the whole workflow
Build an LLM workflow you can evaluate
A dependable AI workflow connects model execution to evidence about whether the task was completed well. That requires more than choosing a model: inputs must be prepared consistently, outputs checked, and unsuccessful attempts retained in the operational record. Use this guide to plan a small workflow for your own application. Define what a useful result looks like, compare changes against representative cases, and keep consumption visible at the level of the whole task. The framework works with local inference and documented provider integrations.
Define success with representative cases
Write the acceptance rule before collecting a headline quality score. A routing task needs correct labels and a policy for uncertain cases. An extraction task needs supported facts and valid references. A summarization task needs coverage of the requested points without introducing unsupported claims.
Build a compact development set containing ordinary inputs, missing information, ambiguous examples, and important edge cases. Keep a separate evaluation set for assessing the final candidate. If related records appear in both development and evaluation, their similarity can make the task easier than future traffic.
Record each example's source and expected outcome. Revisit the annotation guidance when reviewers disagree, and report the size of each evaluated group. An unexplained average can conceal a workflow that fails on one language, input source, or class of document.
Give each processing stage a contract
Separate input assembly, model execution, output validation, and application action. Each stage should state what it accepts and what it guarantees to the next stage. A generated object becomes an actionable result only after the required checks succeed.
- Assemble relevant context with a recorded revision.
- Check input limits and reserve sufficient output capacity.
- Execute using the selected model and settings.
- Validate structure, references, and task-specific meaning.
- Return a terminal state with the appropriate result.
Keep failures attached to their stage. An oversized prompt needs a different remedy from an unsupported claim. Allow correction attempts only under a bounded policy, and validate the replacement result again. Application permissions should remain enforced by the surrounding system, even when the model proposes a plausible action.
Measure quality and consumption together
Connect every attempt to one logical task. Track the model, prompt and schema revisions, completion state, elapsed time, and reported usage. Separate preliminary token estimates from actual reported consumption. Preserve provider-specific categories when a single normalized total would hide how work was counted.
| Question | Useful record |
|---|---|
| Did the task finish correctly? | Validation outcome and reviewed quality |
| Why did it take longer? | Queue time, execution time and attempt count |
| What changed? | Model, prompt, source and schema revisions |
Compare candidate changes using the same cases and acceptance rules. A shorter answer can reduce consumption while omitting a required point. A faster response can still fail validation. Optimize for useful completed tasks and inspect the error categories before deciding what to change next.
Use the official reference for the documented interface; the workflow recommendations above are Tensor API Lab guidance.