An AI workflow is useful only within a task that can be described and assessed. Start with the work a user needs completed, the evidence required for an acceptable result, and the consequences of an error. Then examine the complete process, including supplied data, model access, connected tools, and human review. This framework helps compare workflow proposals on a consistent basis and identify what still needs testing. It offers an evaluation method rather than a ranking of products or a claim that a particular system is suitable for every task.
What to examine
Define the input, expected output, intended user, and permitted actions. Translate success into conditions that a reviewer can inspect. Specify which errors make an output unusable, then preserve acceptable alternatives so an evaluation does not confuse one preferred wording with the only valid way to complete the task.
Account for preparation, generation, tools, retries, checking, and correction. Keep measured usage distinct from estimated future costs and attach dates to pricing assumptions. Compare the effort required for accepted work, with unresolved failures visible, so the evaluation covers the whole process that a user would actually maintain.
Assign responsibility for reviewing consequential outputs and resolving uncertain evidence. Define what reviewers inspect, which actions require approval, and when processing stops. Check that the proposed reviewer has the time, source material, and authority needed to carry out that role within the intended operating process.
A practical review sequence
Write a bounded task description and acceptance checklist. Identify the relevant users and the result they need. Define prohibited actions and material errors before viewing demonstrations so the comparison follows the task's requirements instead of adapting its standard to the most impressive output.
Choose authorized examples covering routine inputs, difficult inputs, and missing information. Record the expected evidence for a passing result. Preserve failures and variation across runs, and identify any part of the real workload that the available evaluation set does not yet represent.
Capture the configuration, output, time, usage, retries, and review effort for each case. Apply the same acceptance criteria across candidates. Explain any extra preparation or manual help, then keep one-time setup work separate from the recurring effort required to produce an accepted result.
State which uses the evidence supports and what remains untested. Assign review and escalation responsibilities, including a response to excess review demand. Identify changes in models, data, permissions, or task scope that would require another evaluation before extending the workflow's intended use.
Primary-source context: Artificial Intelligence Risk Management Framework (AI RMF 1.0). Use it alongside the asset-specific records relevant to your review.
Questions to resolve
Define the task, input conditions, acceptable result, and consequential failure cases first. Build a representative evaluation set around those requirements. Product features become relevant when they help explain performance on the specified work, the operating effort required, or the limits of the proposed workflow.
Use a consistent accounting boundary that includes preparation, tool use, retries, and human effort. Track the number of outputs meeting the acceptance rule and explain missing inputs to the calculation. Compare measured results with dated assumptions rather than presenting a projected cost as an observation.
Revisit the evidence when a material component or intended use changes. Examples include a different model, revised data source, broader permissions, or a new output audience. Preserve the earlier configuration and findings so the next review can explain what changed and which conclusions remain applicable.
Read the Lab
A persuasive demo is the beginning of an AI review. Define the task, build representative tests, compare outputs without brand labels, track failures and costs, and preserve enough detail to understand what changed between evaluations.
Read the guide: A Practical Evaluation Workflow for AI, LLMs, and PromptsDifferent asset names can conceal the same underlying dependencies. Build an exposure map that connects holdings to custody, counterparties, markets, revenue sources, and operating costs, then use simple scenarios to identify what needs closer review.
Read the guide: Build a Digital Asset Portfolio Exposure MapRelated screening lenses
Questions worth asking
Explore a new asset class or suggest a topic for the Lab.
