Build an evaluation set around the errors that could change a research conclusion.
An AI-generated research note can be fluent and still fail its most important task. It may cite a relevant document but the wrong passage, compare different periods, or turn a qualified statement into a definite conclusion. Reviewing the prose alone will not reveal every problem.
Define the intended job before judging the output. A system that extracts reported figures should be assessed differently from one that drafts possible follow-up questions. The first needs exactness and context; the second needs useful questions without pretending they are established facts.
Build a small, difficult review set
Choose documents that represent the workflow, including the cases that tend to break it. Include amended reports, tables with footnotes, multiple currencies, renamed segments, and passages containing explicit uncertainty. Add documents that do not contain the requested answer.
Keep the expected result beside the source. A useful expected result may be “the evidence is insufficient” or “these two periods cannot be compared directly.” The evaluation should reward a correct boundary as well as a correct extraction.
Check the link between claim and passage
For each important output, ask four questions. Does the cited document exist? Does the referenced passage contain the relevant information? Does the output preserve the passage’s qualifications? Does the claim require additional reasoning that the draft has left unstated?
Classify a wrong source separately from an overstated interpretation. They need different fixes. Improving retrieval will not necessarily repair a workflow that systematically removes uncertainty from the language it summarizes.
Score errors by consequence
A formatting error and an unsupported financial claim should not disappear into the same average score. Record the type, severity, and likely effect of each failure. Decide which errors prevent release, which require human correction, and which are acceptable within a clearly defined task.
NIST’s AI Risk Management Framework provides broader guidance for identifying and managing AI risks. For a research workflow, translate that orientation into explicit review responsibilities, evaluation records, and decisions about how the output may be used. A checklist by itself does not establish that a system is reliable.
Test the whole handoff
Evaluate the interface as well as the generated text. A correct citation is less useful if the reviewer cannot open it. A warning about missing evidence is ineffective if it disappears from the exported brief. Check that the final handoff preserves source links, period labels, and unresolved questions.
Run the same review cases after changes to extraction, retrieval, prompts, or models. Keep the previous outputs so that a new improvement can be checked for regressions elsewhere. Avoid changing the evaluation set every time the system performs poorly.
Keep a human decision visible
The review record should identify who approved an output and what they checked. Human review is a defined task with an accountable result, not a label attached to an automated process. Record the correction when a reviewer changes a conclusion, then add that failure to the evaluation set where appropriate.
Gatani’s research automation engagements begin with bounded tasks and agreed review criteria. The useful question is whether the workflow produces inspectable work under its actual operating conditions.
This editorial collection was published on . Archive dates organize the series.