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AI-generated themes and summaries are useful starting points, not evidence by themselves. To check them properly, keep the original comments, define the analytical scope, trace every finding and quotation to source records, recompute any counts, test whether labels match what customers meant, protect personal information, and require a human analyst to approve the interpretation before it is shared or used.
This approach follows the Government of Canada’s feedback-analysis guidance and broader NIST recommendations on validity, provenance, privacy, fairness, and human oversight. The Canada.ca guidance states that AI can identify patterns and summarize qualitative feedback, but does not replace human judgment. Its reusable prompt, version 1.3, is marked “Human Review Required: Yes” and was last updated September 10, 2026. See the Canada.ca Design guidance.
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What you are validating
An AI output can contain several different claims, and each needs a different check. A theme is a claim that multiple comments describe a related issue. A summary is an interpretation of what the comments mean. A count is a quantitative claim about how many comments meet a rule. A quotation is a verbatim representation of one respondent’s words. These are not interchangeable: a plausible theme does not prove its count, and an accurate quote does not prove the surrounding interpretation.
The original feedback is the evidence. Treat each generated insight as a working hypothesis until you can connect it to specific comments and explain why the connection is justified. A credible finding has a clear, neutral label; identifiable supporting comments; an interpretation consistent with the apparent customer task or problem; and a stated limitation when the comments are brief, ambiguous, incomplete, or unrepresentative.
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Validation is also purpose-specific. A model used to identify navigation problems in a support survey needs different test conditions from one used to inform a safety decision or a policy change. NIST’s AI RMF Measure Playbook recommends defining the socio-technical context, intended users, operating limits, and validity conditions before evaluating a system. Apply that principle to the decision your feedback analysis will support.
1. Preserve the source and define the scope before reviewing the output
Keep an authoritative copy
Store the original comments in an authorized environment before any cleaning, translation, deduplication, or AI processing. Preserve stable identifiers, timestamps or collection periods when they are relevant, the question or task that prompted each response, and the version of any transformed file supplied to the model. Work from a read-only or otherwise controlled copy when possible so that a reviewer can reconstruct what the model actually received.
Do not validate against a hand-picked subset merely because it is easier to read. The Canada.ca prompt instructs analysts to review the full dataset provided. If operational constraints require sampling, record the sampling rule, exclusions, and resulting sample size so that a reader can tell what the conclusions do and do not cover.
Write a one-paragraph scope statement
Before looking at the answer, record:
- the service, product, or journey being studied;
- the page, feature, channel, or task associated with the comments;
- the collection date range and relevant market or language;
- the intended audience for the analysis;
- the decision the findings may inform; and
- the precise output requested, such as themes, barriers, representative quotations, or verified counts.
This prevents scope drift. A summary of a checkout survey should not silently become a statement about all customers, and comments from one release should not be treated as evidence about a later interface without a reason to do so.
Remove or control personal information first
Review the data-handling rules that apply to your organization and jurisdiction before sending comments to an AI service. Remove unnecessary names, email addresses, account numbers, health details, payment information, precise locations, and other personal information. Use only organization-approved tools and environments, and confirm retention, access, and deletion settings. Privacy is a control at ingestion, not something to add only when publishing the final report.
If a personal detail is essential to the question, document why it is needed and restrict access to the smallest practical group. A reviewer should never paste an identifiable comment into an unapproved tool simply to obtain a faster summary.
2. Set review rules before you see the answer
Predefine what will count as a theme and what evidence a finding must show. This makes the review less vulnerable to confirmation bias: you are testing the output against rules rather than deciding after the fact that an attractive interpretation is acceptable.
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Define a theme
Write a short rule such as: “A theme is a recurring customer-described issue or goal that can be supported by at least one identifiable comment and expressed in neutral language.” You may choose a stricter recurrence threshold for decisions that require prevalence estimates, but state that threshold in advance. Distinguish an issue from a proposed solution; customers may describe a problem without endorsing the remedy the AI suggests.
Define how ambiguity is handled
Decide whether comments that are too short, sarcastic, contradictory, or missing context will be coded as uncertain, excluded from a particular claim, or reviewed by a second analyst. The Canada.ca prompt tells the model to avoid over-interpreting short or ambiguous comments and to use only the supplied dataset rather than outside assumptions. Apply the same discipline to human reviewers.
Decide whether counts are in scope
Counts require a coding rule and a denominator. Specify whether one comment can belong to several themes, whether repeated submissions count once or more than once, how blank responses are treated, and whether the denominator is all received comments or only comments that answer a particular question. The Canada.ca reusable prompt has a workflow in which it says not to calculate theme prevalence or exhaustive counts; that is a design choice for that prompt, not a universal ban on counting. If your decision needs prevalence, define and independently verify your own method.
3. Build an evidence trail for every finding
Ask the AI output to include a source identifier for each theme, claim, and quotation. If the tool cannot provide identifiers, create the mapping manually during review. A reviewer should be able to move from a sentence in the report to the exact original comments that support it, without relying on a model’s explanation of its own reasoning.
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Use a claim-to-comment matrix
A simple review sheet can contain these fields:
- generated finding and its neutral label;
- source comment IDs or secure links;
- exact quotation, if one is used;
- the customer task or problem the comment appears to describe;
- your interpretation of what the comment supports;
- uncertainty, contradiction, or missing context; and
- reviewer decision: supported, narrowed, unverified, or removed.
Require the model and the analyst to use only the supplied comments for evidence. The Canada.ca guidance explicitly says not to invent, alter, or paraphrase representative comments. A quote that sounds clearer after editing is no longer a verbatim quote; put any explanation outside quotation marks and label it as interpretation.
Test the strength of the link
For each source comment, ask: does it actually describe the issue named in the theme, or is the connection inferred? Does it support the entire sentence or only one clause? Is the comment being used to claim a cause, sentiment, or desired outcome that the customer did not state? If the answer is uncertain, narrow the wording or mark the finding unverified.
4. Check whether themes match customer intent
Theme names should describe the customer’s reported experience rather than the organization’s preferred explanation. “Customers cannot find the refund policy” is more testable than “Customers need better education,” unless the comments explicitly support the latter. Keep issue names consistent: do not split “cannot locate policy” and “refund information is hard to find” into separate themes unless the comments show a meaningful difference.
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Separate distinct dimensions
One comment can contain several ideas. For example, a customer may say that a form is difficult to find and that it asks for information they do not have. Those are potentially different navigation and eligibility issues. The AI may merge them because both sound negative, or split them into too many micro-themes. Compare the proposed grouping with the task the customer was attempting and decide whether the distinction changes the decision you need to make.
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Flag conclusions about motives, demographics, causes, or solutions that are not in the text. A complaint that “the page timed out” supports a report of a timeout; it does not by itself establish a server defect, deliberate design choice, or effect on every user. Ask the analyst to distinguish what the comment says, what pattern is visible, and what remains a hypothesis requiring other evidence.
Check neutral language
Replace loaded labels such as “careless users” or “fraudulent requests” with descriptions of observable behavior. Neutral wording improves consistency and reduces the chance that a model’s stereotype becomes an organizational decision. NIST’s trustworthiness guidance treats validity, transparency, explainability, privacy, and fairness as characteristics to balance in context, not as a single score. See NIST’s AI RMF trustworthiness guidance.
5. Verify quotations and counts independently
Compare quotations character by character
Open the original record and compare every quoted word, including negations, qualifiers, spelling, and punctuation where those details affect meaning. Confirm that the quote comes from the stated comment ID and that redactions are clearly marked. A model can produce a fluent sentence that combines fragments from different comments or changes “can” to “cannot”; either error can reverse the conclusion.
Do not call a paraphrase a representative quote. If you need a shorter version, retain the original in the evidence record, use an explicit ellipsis only under your organization’s quotation rules, and label the public text as a paraphrase.
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Apply the predeclared coding rule to the source data, not to the AI’s list. Record the numerator, denominator, overlap rule, exclusions, and reviewer. If a comment can receive multiple theme labels, theme percentages will not necessarily add to 100 percent. If you are reporting a sample rather than a census, describe it as a sample and include the sampling method.
Never infer frequency from the amount of text devoted to a theme. A long AI paragraph may represent one detailed comment, while a short label may represent many. The Canadian prompt’s instruction to avoid prevalence calculations in its intended workflow is a reminder that counts should be produced only when the task and method support them.
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6. Test dataset coverage, language, and representativeness
Confirm that the model saw the intended files, rows, languages, and date range. Check for truncation, token limits, failed uploads, duplicate records, missing pages, and accidental filtering. Compare the input row count and language distribution with the output’s claimed coverage. If the model processed batches, inspect whether the same theme was named differently across batches and reconcile labels before counting.
Handle bilingual and multilingual feedback deliberately
Review original-language comments where meaning, politeness, idiom, or negation may be lost in translation. A translated summary can be useful, but it is not a substitute for checking the source wording. Record which language was used for coding and whether a qualified reviewer checked translations. Canada.ca’s feedback guidance specifically calls for attention to bilingual handling and interpretation accuracy.
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Compare the feedback set with the population relevant to the decision. Voluntary comments may overrepresent people with unusually strong positive or negative experiences. A model can accurately summarize the supplied data while the supplied data poorly represents customers who never responded. NIST recommends representative data, attention to differences between intended and actual user populations, and evaluation broken down across relevant groups where appropriate. If lawful and necessary, examine whether findings or error rates differ by language, channel, accessibility need, geography, or other decision-relevant group.
Do not manufacture subgroup conclusions
Only report a subgroup pattern when the data and collection method support it and privacy rules permit it. Small cells can identify individuals or create unstable conclusions. If a group is not adequately represented, state that limitation instead of treating silence as evidence that the issue does not exist.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Document provenance, limits, and the review record
Keep a versioned record of the model or system used, prompt, input file identifier, preprocessing, date, reviewer, coding rules, and output version. NIST’s Generative AI Profile recommends documenting the origin and provenance of generated content, reviewing generated outputs against organizational guidelines, and examining feedback loops between provenance processes and human reviewers. Its guidance is general-purpose; here, the practical application is a traceable record from each published claim back to source comments and the review decision.
Write down known operating limits: supported languages, maximum input size, excluded fields, sampling approach, and the kinds of decisions the analysis must not make without additional evidence. If the output was generated in batches or revised after review, retain the earlier version and note what changed.
Use explicit status labels
- Supported: direct source comments substantiate the stated claim.
- Narrowed: evidence supports a more limited wording than the generated version.
- Unverified: the source cannot be located or does not justify the claim.
- Removed: the claim is unsupported, misleading, outside scope, or unsafe to share.
These labels prevent an attractive but weak insight from surviving merely because it appeared in an automated draft.
8. Require human review before sharing or acting
A human analyst should review the complete evidence matrix, not just a selection of polished examples, and approve the final interpretation. The reviewer should be familiar with the service context, the collection method, applicable privacy rules, and the decision the report may influence. For high-impact decisions, use a second reviewer or an escalation path for disputed interpretations.
Before publication or operational use, check that every material claim has evidence, every quotation is exact, counts follow the stated rule, limitations are visible, and sensitive information has been removed or lawfully handled. The Canada.ca guidance gives this disclosure example: “Analysis was assisted using AI. Findings were reviewed and validated by a human analyst.” Adapt the wording to your organization’s policy and explain what the human review covered.
Decide whether the finding is fit for its use
A theme may be adequate for generating interview questions but inadequate for changing eligibility rules. Match the review depth to the consequence of the decision. For consequential uses, require stronger representative testing, documented uncertainty, and independent corroboration such as usability observation, support records, or a quantitative measure. Do not present a qualitative pattern as causal proof merely because it is repeated.
9. Revalidate when conditions change
Repeat the checks when the model, prompt, preprocessing, input mix, language, customer population, product experience, or intended use changes. A workflow that worked on short English survey comments may fail on multilingual transcripts or long-form complaints. NIST recommends documenting operating limits and evaluating with realistic test sets that represent expected conditions; its trustworthiness guidance also recommends documenting test methodology and using representative data.
Maintain a small, access-controlled validation set containing examples of clear comments, ambiguous comments, negations, mixed issues, sensitive information, and multilingual wording. Use it to detect regressions after a prompt or model change, but do not treat it as proof that performance generalizes to every future dataset. Connect quantitative checks, such as quote-match and coding agreement, with qualitative review by analysts and affected communities when appropriate.
A practical review worksheet
Use the following sequence for each AI-assisted feedback report:
- Identify the input: record the source file, date range, service area, languages, row count, and processing version.
- Confirm authorization: verify the tool, environment, retention settings, and personal-information controls.
- Read the task definition: confirm the requested themes, quotation rules, counting requirements, audience, and decision context.
- Map every finding: attach source IDs and exact text to each theme and material claim.
- Test interpretation: compare the label with the customer’s apparent task, separate issues, and remove inferred motives or causes.
- Recompute metrics: apply the documented coding rule and denominator to verify counts and sample sizes.
- Inspect coverage: check missing records, duplicate rows, batch differences, languages, and relevant population groups.
- Record uncertainty: mark unsupported, ambiguous, or privacy-sensitive findings and narrow or remove them.
- Obtain sign-off: have a qualified human analyst approve the final wording and limitations.
- Archive the trail: retain the prompt, output version, evidence matrix, decisions, and disclosure required by policy.
Common failure modes and the corrective action
| Failure mode | Why it is risky | Corrective action |
|---|---|---|
| A polished theme has no source IDs | You cannot test whether the comments support it. | Require a claim-to-comment mapping; narrow or remove any untraceable finding. |
| The AI reports a precise percentage | The denominator or coding rule may be wrong or unstated. | Recalculate from the original data and publish the rule and denominator. |
| A quotation is slightly rewritten | A changed qualifier or negation can alter customer intent. | Compare with the original and label paraphrases as paraphrases. |
| One ambiguous comment is treated as a clear motive | The interpretation exceeds the evidence. | Mark it uncertain, seek context, or exclude it from the claim. |
| Only the most memorable comments are reviewed | Outliers can be mistaken for the overall pattern. | Check the full intended dataset or document a defensible sample. |
| Personal information is removed after analysis | Exposure has already occurred. | Minimize and protect data before using an AI tool. |
| A workflow is reused after a major change | Validity depends on context, population, language, and use. | Run a new validation under the changed conditions. |
What a defensible final report contains
A reader should be able to understand what was analyzed, how it was processed, and where uncertainty remains. Include the service and date range, dataset coverage, analytical task, theme definitions, evidence links or IDs, quotation policy, counting method if applicable, privacy treatment, reviewer name or role, known limitations, and the intended use of the findings. State when AI assisted the work and what human validation consisted of.
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