Answer
Use AI assistance for a bounded task whose inputs and outputs a reviewer can inspect. Define what the tool may suggest, what it must leave unresolved and who may release the result. Test ordinary and difficult cases, record corrections and repeat affected checks after changes. A fluent answer is not proof of a correct design.
An AI-generated summary and an AI-generated roof model need different checks. A summary can omit a customer condition. A roof model can represent a site differently from the evidence provided. Calling both “AI assistance” does not define what your team needs to verify.
Start with one task and its next user. This guide proposes a review process, not a benchmark, a solar engineering standard or evidence that a particular product performs the tasks described.
Specify the task before testing the tool
| Task field | Example question |
|---|---|
| Input | Which files, records or imagery may the tool use? |
| Requested output | Is it extracting fields, drafting text or suggesting a layout? |
| Allowed action | May it suggest changes, or can it modify the working record? |
| Required review | Which source can a reviewer compare the output against? |
| Unresolved state | How should missing or conflicting input be displayed? |
| Release boundary | Who can approve the next use of the output? |
Choose a task narrow enough that a reviewer can identify failure. “Help with solar design” is not a useful acceptance criterion. “Extract the site address and billing period from these documents, with a reference to each source” is a testable request.
Separate suggestion from decision
An extracted value, suggested arrangement and reviewed project decision should have distinguishable states. Do not let a plausible suggestion become the controlling record simply because it filled an empty field.
For a document task, ask the reviewer to check omissions as well as incorrect statements. For a design-related task, identify the qualified review needed for its particular outputs. This guide does not provide dimensional tolerances, electrical limits or approval criteria.
NIST’s AI Risk Management Framework is intended for voluntary use and addresses trustworthiness considerations in the design, development, use and evaluation of AI systems. It is a general risk-management reference, not certification of a solar tool or a replacement for applicable professional review.
Build difficult cases into the evaluation
Use representative records you are permitted to share. Include conflicting addresses, missing pages, ambiguous images and changes to previously accepted input where they are relevant to the task.
Illustrative test: ask a tool to summarize a customer’s requested option from notes that describe two alternatives. Check whether it preserves the distinction between “asked to compare” and “selected.” A reviewer should be able to trace the summary to the original wording.
For each case, record the input version, tool and configuration, expected behavior, actual output, correction and next decision. A result can be useful while still failing an important release condition.
Decide what happens when evidence is missing
Define a return route instead of rewarding a complete-looking answer. The tool or operator should flag the missing source, identify the affected output and send the question to the responsible owner.
Keep customer data and commercially sensitive material within your organization’s approved tool and account arrangements. Ask the vendor about the applicable data handling terms before submitting project records.
Review changes before expanding use
Log errors by task and consequence: omitted condition, invented detail, wrong source, stale input or unsupported conclusion. Record review effort too. A shorter generation step does not establish a faster complete workflow if correction and checking increase.
Repeat affected tests when the tool, prompt, input format or working process changes. Define the evidence needed to expand the task rather than treating one successful demonstration as general validation.
Record the reviewer’s release decision
Use a release record for each consequential output, separate from the pilot scorecard. Keep the input and output versions, intended next use, reviewer’s name and role, sources actually checked, findings, unresolved items and decision. “Human reviewed” alone does not explain the scope of the check.
| Release decision | What the next user should receive |
|---|---|
| Return for missing evidence | Missing source, dependent outputs and the person responsible for obtaining it |
| Accept for a limited purpose | Exact version, permitted use and remaining limitations |
| Reject | Failure evidence and the fallback route |
| Request qualified project review | Output and supporting records, with no implied engineering or external approval |
Illustrative release: an extracted billing-period field can be accepted for an intake record after comparison with the bill. That decision does not authorize a production estimate or financial promise derived from other unchecked fields. If a source or model changes, identify and recheck affected decisions; a prior approval belongs to its recorded version and purpose.
For candidate tasks and source-verification checks, see AI assistance in solar work and expert review. This page focuses on who releases the result and what the next user may rely on.
Ask product-specific questions
When evaluating Clara AI, ask for the current input requirements, output scope and manual correction workflow. Ask which decisions remain outside the feature. Do not infer these details from another vendor’s AI demonstration or from the term “AI-powered.”
Bring an authorized sample and an expected result to a guided demo. Record what was demonstrated and what still needs verification in your environment.
Frequently asked questions
Can a good-looking AI output be released immediately?
Appearance alone does not establish correctness. Compare the result with its source and the task’s acceptance criteria. Route technical or other consequential decisions to the appropriate reviewer before the output is used for a commitment.
What should happen when the input is incomplete?
Keep the gap visible, identify which result depends on it and ask the responsible person for the missing evidence. Do not allow an inferred value to look like a verified source value.
How should we measure an AI pilot?
Record task completion, error types, correction effort and review effort on representative cases. State the tested tool, configuration and inputs. Avoid an overall accuracy or productivity claim that the test design does not support.
Sources
Primary research and reference material used for this desk-research article.
Where this fits
This article is part of SurgePV's Solar Business & Operations hub, which works through the topic from first principles to the decisions a project team actually has to make.
