Define the summary’s job
A short description of a conversation, an explanation of its development and a recommendation for leadership are different tasks. Specify what the generated output is expected to do and who will review it before it influences a decision.
When evaluating narrative intelligence software, keep a reference set and a written assessment rubric. Include ordinary examples, disagreement, ambiguity and material that challenges the dominant interpretation. The model’s fluency should not determine whether the result passes.
Ask what material enters the summary, how the source window is defined and how missing or excluded records are represented. A well-written summary can still give an incomplete view of the collection.
Check grounding at the level of a claim
Pick each important statement in the summary and trace it to supporting material. Confirm whether the statement is an observation, a synthesis or an inference. Ask whether the linked records support the wording used and whether a second reviewer can reproduce the reasoning.
Test stance explicitly. Quoting a claim to reject it should not automatically become support for that claim. Include a paraphrase and an ambiguous example, then record how the system groups them and how a reviewer can revise the result.
| Check | Question |
|---|---|
| Grounding | Which records support this sentence? |
| Stance | Does the source endorse, reject or report the claim? |
| Uncertainty | What is unresolved and why? |
| Correction | Can a reviewer change the grouping or conclusion? |
Test the behavior around uncertainty
Ask the system to handle a case where the available material does not establish origin, intent or attribution. A useful result should preserve that limit. Record whether the wording becomes stronger than the evidence when the output is shortened or moved into an executive format.
Compare repeated runs only under documented conditions. A different collection, prompt, model or configuration can change an output. Preserve the tested version and enough context to understand what changed.
Would the recipient understand which statements are directly observed and which depend on interpretation?
Use risk governance as a practical review discipline
NIST’s AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design, use and evaluation. It offers useful context for planning evaluation and ownership around AI-enabled analysis.
For this task, translate that concern into a specific process: define the use, test representative outputs, record limitations, assign a reviewer and revisit the configuration when the task changes. Citing a framework does not establish that a vendor or a summary is certified or suitable.
For required controls, ask how prompts, submitted material, generated outputs and review records are handled in the proposed product. Have the relevant internal owner assess the response under the organization’s requirements.
Connect the test to the operating workflow
Measure the human review needed to turn the summary into a source-linked brief. Record recurring errors, corrections and examples that need specialist interpretation. Include those findings in the pilot and commercial scope.
Decide which outputs can support routine review and which require additional approval. Keep an accessible path back to the original records and the reviewer’s assessment so an automated summary remains part of an accountable analytical process.
Questions for the brief
Should we ask the model to give a confidence percentage?
A number is useful only when its meaning and basis are defined for the task. Ask what the value represents and how it relates to source quality, uncertainty and the decision. Inspect the explanation rather than treating a precise percentage as established accuracy.
Can a source-linked summary still be wrong?
Yes. A linked source may not support the sentence, may be quoted with the wrong stance or may represent only part of the available material. Review the relationship between each material claim and its evidence.
Our leadership wants very short AI briefs. How can we preserve important limits?
Keep the core finding, relevance, uncertainty and next owner in the short format. Provide a source-linked detail layer and test whether shortening the briefing changes the strength or meaning of the original assessment.