How to prepare
How to Prepare for an AI Reliability Assessment
The best assessments start with one focused AI workflow, clear success criteria, and enough safe access to test realistic examples.
Start with one workflow.
A focused scope makes the results more useful. Instead of testing “our AI,” choose one workflow: a support assistant, document search tool, classification pipeline, extraction workflow, sales assistant, onboarding helper, or internal agent.
Choose the workflow
Name the AI feature, who uses it, and what job it is supposed to complete.
Define success
Share examples of good answers, bad answers, known edge cases, and unacceptable failure modes.
Provide safe access
Use a test account, staging environment, sample data, exported prompts, or a safe workflow path whenever possible.
Review the findings
Use the report and reusable test cases to prioritize product, prompt, retrieval, or workflow improvements.
What to have ready before the assessment.
Product context
Who uses the feature, what they expect, where the AI sits in the product, and what decisions or workflows depend on it.
Example inputs
Realistic customer questions, documents, support tickets, records, prompts, or tasks the AI should handle well.
Expected behavior
Examples of correct answers, acceptable uncertainty, refusal rules, formatting requirements, and known problem areas.
Access boundaries
What can be safely tested, what data must be excluded, and whether production access is unnecessary or off-limits.
What companies get back.
The goal is not vague AI advice. The goal is a practical evidence package your team can use after the engagement.
- Reusable test cases for one AI workflow
- Accuracy, consistency, latency, and cost observations
- Failure categories and examples
- Prioritized technical recommendations
- Final written report
- Scheduled results-review meeting