Build an AI workflow your team can evaluate and maintain.

Define the output before choosing the model

Take a recurring task with a reviewable deliverable. For a case chronology, the output might be events with a date, description, source document, page reference, and an explicit unknown state when the evidence is incomplete.

Write down what counts as a correct event, which omissions matter, and which fields require human judgment. Separate a valid JSON object from a factually correct record. A schema validator can establish only the first.

Define the allowed actions too. The first release may prepare a draft without sending it or updating the system of record. That makes the review boundary part of the workflow rather than an informal instruction.

Build an evaluation set that can expose failure

Collect authorized examples representing the real input distribution: clean files, scans, duplicates, ambiguous dates, missing pages, and conflicting statements. Include cases where the correct response is to leave a field unanswered.

Have a knowledgeable reviewer establish expected outputs and evidence. Resolve disagreements in the labels before using them to judge a model. A disputed reference answer is not a reliable pass/fail signal.

Keep development examples separate from a held-out set. Repeatedly changing prompts after inspecting held-out failures turns those examples into training material. Create a fresh holdout before claiming the improvement generalizes.

Measure extraction and the human work it creates

For event extraction, precision is correct extracted events divided by all extracted events. Recall is correct extracted events divided by all expected events. Define matching rules and duplicate handling before scoring.

If a synthetic test contains 100 expected events and the system returns 90, of which 81 are correct, precision is 90% and recall is 81%. That distinction tells you whether reviewers face false entries, missing entries, or both.

Track unsupported claims separately from safe blanks. Record source-reference accuracy, time spent correcting the draft, and the share of outputs reviewers reject. Fluent prose can conceal a poor extraction result.

Compare total human handling time with the baseline task, including preparation, review, and repair. Report the range across cases. A faster first draft is not sufficient evidence that the workflow saves time.

Package the operating procedure

Give the user a defined input path, a recognizable output, and a way to flag failure. Train with an ambiguous example as well as a successful one. The reviewer should know how to check evidence and when to fall back to the original process.

Version the model identifier, prompt template, retrieval configuration, tool schemas, and evaluation set. A workflow release is that combination. Changing one component can alter behavior even if the model name stays the same.

Keep a small regression set covering previous failures. Add new cases without silently rewriting the old expected results to match the latest output.

Release with an owner and a rollback

Choose acceptance thresholds before the pilot. A high-stakes field may require no observed unsupported answers in the test set, while routine formatting may tolerate repair.

Passing a finite test is evidence about that sample, not a guarantee.

Start with supervised use, monitor rejected drafts and correction effort, and compare them with the evaluation results. Unexpected input types should enter a review queue rather than expanding the scope automatically.

Assign a person who can pause the workflow, restore the prior configuration, and explain the change history. The system becomes useful when the team can run, challenge, and repair it without depending on its original builder.