Introduction
Generative AI systems can produce useful results while also inventing details, misreading documents, or failing on edge cases. An evaluation process must be designed for the specific business task, not merely for whether the output sounds convincing.
Use these practices as a decision framework, not a rigid technology checklist. The appropriate controls depend on the consequences of errors, the type of information handled, and the team responsible for monitoring the resulting service.
Specify the intended decision
Describe what the generated answer will help someone do. Define the cost of incorrect, incomplete, outdated, or inappropriate output.
An internal brainstorming suggestion has different acceptance standards from extracted invoice totals or information shown to a customer.
Apply this to a real example rather than an idealized workflow. For specify the intended decision, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Create representative examples
Collect anonymized or synthetic cases spanning ordinary tasks, difficult inputs, missing data, and conflicting instructions.
A receipt extractor should be tested on blurred photos, discounts, taxes, multiple currencies, and unusual merchant layouts, not only clean samples.
Apply this to a real example rather than an idealized workflow. For create representative examples, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Define measurable criteria
Choose checks for factual accuracy, completeness, format, relevance, and unacceptable content. Separate requirements that can be automatically tested from those needing human judgment.
A JSON response may pass schema validation and still contain an invented customer identifier. Both structure and meaning matter.
Apply this to a real example rather than an idealized workflow. For define measurable criteria, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Use appropriate reference answers
Establish trusted source records or carefully reviewed expected outcomes for the evaluation set.
Where more than one acceptable answer exists, document the range and reasoning rather than forcing an arbitrary string match.
Apply this to a real example rather than an idealized workflow. For use appropriate reference answers, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Inspect failure patterns
Group errors by cause: missing context, ambiguous prompt, unsupported tool result, faulty reasoning, or integration behaviour.
If failures cluster around foreign currencies, update tests and workflow rules rather than declaring the entire model unusable.
Apply this to a real example rather than an idealized workflow. For inspect failure patterns, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Evaluate human review effort
Measure how long users spend correcting suggestions, how often they reject results, and whether errors go unnoticed.
A faster draft can create negative value when reviewers must reread the source line by line. Measure total task completion time.
Apply this to a real example rather than an idealized workflow. For evaluate human review effort, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Monitor changes over time
Re-run evaluations after changing models, prompts, data sources, or preprocessing rules. Record the version that produced each result.
A provider upgrade may improve typical answers while introducing regressions in rare but important edge cases. Release decisions need comparable evidence.
Apply this to a real example rather than an idealized workflow. For monitor changes over time, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Decide when not to automate
Identify tasks where the consequences of a mistake or lack of verifiable evidence outweigh the expected benefit.
Keep a direct human workflow for decisions requiring accountable judgment. Automation should help users understand and control the process.
Apply this to a real example rather than an idealized workflow. For decide when not to automate, write down what evidence would show success, which person is accountable, and which failure or unusual case should be tested. Review the finding with people who do the work, then adjust the approach before expanding use.
Practical takeaways
Further reading
These primary resources provide additional detail and guidance.
Related services
Explore the services behind these topics:
- Data & AI — Practical data and AI solutions that automate processes and reveal useful insights.
Continuing the conversation
Use these steps as a starting point for your own context, risks, and priorities. We can help you assess the situation, plan practical improvements, and build toward your goals.
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