Keep every comment attached to the context that explains it, remove personal information under your organization’s rules, define the question and tagging scheme before analysis, and require evidence for every AI-generated finding. AI is useful for sorting large volumes of feedback and suggesting themes, but a human analyst must verify counts, intent, quotations, and limitations against the original comments.
What “preserving context” means
A customer comment is rarely meaningful by itself. “I could not find it” might describe a search problem, a missing feature, a confusing page, or an employee who was looking in the wrong service area. The surrounding facts determine which interpretation is reasonable.
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Store the comment and its context as separate, clearly named fields. Canada.ca design guidance gives service area, page title or URL, and date range as lightweight context that improves AI results. Add other fields only when they are needed for the decision and permitted by your organization’s data rules.
Context fields that usually help
| Field | What it contributes | Important qualification |
|---|---|---|
| Feedback ID or safe pseudonymous key | Lets an analyst trace a finding back to one record without exposing an identity. | Do not use an account number, case number, email address, or another direct identifier as the key. |
| Original comment text | Preserves the customer’s wording for coding and quotation checks. | Keep an unmodified source copy in an approved location; send only the permitted version to an AI tool. |
| Service, product, feature, or page | Shows which part of the experience the comment concerns. | A page title or URL is useful context, but a URL may itself contain sensitive parameters; remove them when required. |
| User task or question | Explains what the person was trying to accomplish, which is often more actionable than sentiment. | Use a neutral description rather than guessing the person’s intent from tone alone. |
| Channel or collection method | Distinguishes surveys, support contacts, interviews, app prompts, and other collection modes. | Different channels attract different kinds of respondents, so do not treat them as interchangeable samples. |
| Date or date range | Allows analysis of changes after a release, policy change, campaign, or incident. | Keep the time zone and collection window where they affect interpretation. |
| Language | Helps reviewers assess translation, wording, and language-specific themes. | Do not infer proficiency, nationality, or identity from language. |
| Journey stage or issue category | Supports comparisons such as discovery, completion, payment, or follow-up. | Use this as a coded field, not as a substitute for the original comment. |
| Sampling or inclusion notes | Records why a response is present, excluded, duplicated, or weighted differently. | Document the rule used; do not silently remove inconvenient comments. |
This list is an application of the context principle, not a universal required schema. Collect the minimum information needed for the intended analysis. More metadata can increase privacy exposure and create misleading precision if it is incomplete.
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Start with the decision, not the tags
Write the decision or learning need before choosing categories. Examples include identifying which tasks fail, understanding why customers abandon a process, or checking whether a known issue changed after an update. State who will use the result and what action the result could support.
Choose a question that can be answered from comments
- Task question: Which customer tasks are blocked, delayed, or completed with difficulty?
- Cause question: What reasons do customers give for the problem?
- Change question: Did the language or frequency of a theme change during a defined period?
- Service question: Which service areas generate requests for clarification or workarounds?
A question such as “What do customers think?” is too broad to produce consistent coding. Narrow it to a unit of analysis, period, audience, and intended use. The question also determines whether one comment may receive several themes and whether a task-based or issue-based scheme is more appropriate.
Build a tagging scheme that retains separate facets
Read a sample of comments before finalizing the codebook. Qualitative analysis is iterative: immerse yourself in the data, draft codes, test them, discuss ambiguous cases, revise definitions, and recode affected comments. A retrieval tool can find text, but analysts decide whether a pattern is coherent and meaningful.
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| Approach | Best fit | Risk to manage |
|---|---|---|
| Task-based tags | Feedback spanning several pages, steps, or jobs, where the customer’s goal is central. | A single task can contain several distinct problems, so keep issue type in another field. |
| Issue-based tags | A single page or dominant task with recurring problems such as unclear wording, access failure, or missing information. | Different tasks can be collapsed into one issue unless task context remains available. |
| Two-level tags | Large datasets that need a broad theme and a more precise subtheme. | Deep hierarchies can create false consistency; add a level only when it improves a decision. |
Keep dimensions in different fields
Do not create one label such as “frustrated payment failure on mobile.” Store the relevant facets separately, for example:
- Task: submit a payment
- Status: failed
- Issue type: error message
- Device or channel: mobile web
- Customer impact: could not complete
Separate fields let you ask new questions later without recoding every comment. Define each tag with an inclusion rule, an exclusion rule, and one or more examples. State whether tags are mutually exclusive and whether a comment can receive multiple tags. Include an “unclear,” “off-topic,” or “other” option so the model is not forced to invent a fit.
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Prepare and minimize the data before using an AI tool
Use an environment approved for the data and follow applicable consent, privacy, retention, and organizational requirements. The exact legal basis and retention period depend on your jurisdiction and organization; they cannot be inferred from a general preparation checklist.
Remove direct identifiers
Before upload, remove names, email addresses, phone numbers, account identifiers, case numbers, order numbers, and other personal information unless your rules explicitly permit processing them. Replace a necessary reference with a safe token such as customer_042 that cannot be reversed by the model.
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Assess indirect identification
A name is not the only way a person can be identified. A distinctive event, rare occupation, exact date, location, vocabulary, or writing style can point to an individual. Government of Canada qualitative research standards caution that verbatim comments may identify someone indirectly through their content, vocabulary, or style.
Redact sensitive portions when the meaning remains intact. If redaction would destroy the meaning or leave a realistic re-identification risk, exclude the comment from AI input and from public reporting. Keep the decision and reason in an access-controlled project record.
Preserve quotation integrity
Keep the original wording in the controlled source dataset. A model may be allowed to summarize a redacted version, but it must never present a generated paraphrase as a verbatim quotation. If a report needs an exact quote, copy it from the source record after a human checks every word and redaction.
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Give the model an explicit, testable instruction
AI output becomes easier to audit when the prompt defines the role, question, fields, counting rules, evidence requirements, and uncertainty handling. Canada.ca reusable prompt guidance recommends specifying the task and format, how to handle sensitive information, categorization rules, and validation expectations.
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Prompt components to specify
- Role and purpose: identify the analyst role and the decision the analysis supports.
- Context fields: explain which columns describe service, page, task, channel, date, and language.
- Unit of analysis: state whether counts are comments, people, sessions, or another unit.
- Assignment: say whether one comment may receive multiple themes.
- Uncertainty: require an unclear or insufficient-evidence label instead of a guess.
- Evidence: require source IDs and exact excerpts for each proposed theme.
- Quotation rule: prohibit invented, altered, or reconstructed verbatim comments.
- Sensitive-data rule: instruct the model not to reproduce redacted information.
- Output format: define the fields, table columns, or machine-readable structure you will review.
- Limit of inference: prohibit claims about prevalence, causation, demographics, or sentiment that the dataset cannot establish.
Illustrative prompt structure
Role: You are assisting an analyst who is answering this question: [state one decision question].
Dataset fields: feedback_id, comment, service, page, task, channel, date, language.
Rules: assign zero, one, or multiple themes according to the codebook; count comments, not people; mark unclear cases; do not infer causes or demographics.
Evidence: for every theme, return the feedback_id values and copy supporting excerpts exactly from the supplied text. Never invent or silently edit a quotation.
Output: theme, definition, count, feedback_ids, exact excerpts, counterexamples, uncertainty, and suggested follow-up check.
Privacy: do not reproduce redacted personal information.Replace the bracketed instruction with your actual question and keep the codebook with the prompt version used. If you change the rules, save a new version so results from different runs remain comparable.
Validate every finding against the source comments
AI-generated themes are hypotheses until a human checks them. Compare the output with the raw or appropriately redacted dataset before sharing it.
- Reconcile totals. Confirm that the number of assigned comments matches the stated counting rule. Explain comments that are intentionally excluded, duplicated, or allowed multiple themes.
- Trace each theme. Open the cited feedback IDs and verify that each comment actually supports the theme definition.
- Check intent. Read enough surrounding context to distinguish a customer’s goal from a model’s interpretation of tone or wording.
- Audit quotations. Compare every proposed excerpt character by character with the source, including negations, qualifiers, spelling, and redactions.
- Look for omissions and counterexamples. Search for comments that should fit the theme but were missed, and for comments assigned to it that contradict it.
- Review translation and language handling. Have a qualified reviewer check whether translation changed meaning or collapsed culturally specific terms.
- Test stability. Re-run a small, fixed sample after prompt or codebook changes and investigate materially different assignments.
- Decide whether the theme is actionable. A frequent label is not automatically a meaningful problem; confirm that it represents a distinct experience and can inform a decision.
Use other evidence when appropriate: task-success results, analytics, support-call volumes, usability sessions, or operational records can confirm or qualify a pattern. These sources complement comments; a raw comment count alone does not prove prevalence in the full customer population.
Describe the sample, method, and limitations
Every deliverable should let a reader understand what was analyzed and what remains uncertain.
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Document the sample
- Collection source and channel
- Collection dates or date range
- Number of records and the counting unit
- Inclusion, exclusion, deduplication, and missing-data rules
- Languages and translation procedures
- Relevant service, page, product, or journey coverage
Document the method
- Research question and intended audience
- Codebook version and multi-tag rules
- Whether analysis was manual, AI-assisted, or combined
- Prompt or workflow version, if AI was used
- Human review steps and who approved the findings
- How quotations and totals were checked
Document representation limits
Feedback can underrepresent people facing cultural, linguistic, geographic, disability, technology, or socioeconomic barriers. A voluntary support channel may overrepresent people with severe problems; an in-product prompt may miss people who abandon before reaching it. State which groups, channels, tasks, or periods are absent or sparse, and explain how that affects interpretation. UK government dataset guidance treats context, governance, interoperability, suitability, representativeness, and known bias as part of readiness for AI use.
A concise disclosure can say: “Analysis was assisted using AI. Findings were reviewed and validated by a human analyst.” Add the dataset, method, and limitations rather than implying that the disclosure itself proves accuracy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose the right level of automation
| Approach | Use it when | Advantages | Trade-offs |
|---|---|---|---|
| Manual review and coding | The dataset is small, interpretation is detailed, or close source verification is essential. | Maximum control over context, ambiguous intent, and quotation selection. | Requires analyst time and disciplined, documented coding. |
| AI-assisted analysis | The volume is large and rapid candidate themes, grouping, or draft summaries would help. | Accelerates triage and retrieval of potentially related comments. | Requires privacy controls, explicit instructions, and human checks for themes, counts, and wording. |
| Combined workflow | Most operational settings with enough volume to benefit from triage but enough risk to require review. | AI proposes groups; people refine the codebook, inspect evidence, and approve conclusions. | Needs a clear handoff, audit trail, and defined stopping point for human review. |
The CDC Field Epidemiology Manual emphasizes that qualitative analysis requires sufficient time and immersion in the data. Automation can reduce retrieval effort, but it does not remove the need to read, compare, and interpret comments.
Common failure modes and fixes
Uploading a spreadsheet with hidden identifiers
Symptom: The file contains names, email addresses, case numbers, or URLs with identifying parameters. Fix: Create a minimized analysis copy, replace necessary references with safe keys, inspect hidden columns and metadata, and use only an approved environment.
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Symptom: Similar problems receive different labels because the customer’s task changes, or different problems are merged under one broad label. Fix: Split facets into fields, define inclusion and exclusion rules, and recode the sample after revising the codebook.
Letting the model force every comment into a theme
Symptom: Off-topic, ambiguous, or contradictory comments inflate a category. Fix: Permit “unclear,” “off-topic,” and “other,” require an explanation, and inspect those records separately.
Treating frequency as importance
Symptom: The most common label is presented as the most serious issue. Fix: Report counts with the unit and sample, then consider severity, task impact, affected journey stage, and corroborating evidence.
Publishing polished but altered quotations
Symptom: A summary sounds plausible but cannot be found in the source. Fix: Require IDs and exact excerpts, copy quotations only after source review, and label summaries as summaries.
Assuming a name-free comment is anonymous
Symptom: A distinctive story or writing style could identify the author. Fix: assess indirect identifiers, redact or exclude high-risk text, restrict access, and avoid public verbatim publication when meaning cannot be preserved safely.
A practical handoff checklist
- The decision question and intended audience are written down.
- Each comment has only the context fields needed for that decision.
- Direct and indirect personal identifiers have been assessed under organizational rules.
- The source copy is protected and the analysis copy is clearly distinguished.
- The codebook defines tags, examples, exclusions, multi-tag behavior, and uncertainty handling.
- The prompt states counting, evidence, quotation, privacy, and inference rules.
- AI outputs include traceable IDs or excerpts for proposed findings.
- A human checked totals, omissions, intent, translations, and quotations.
- Important patterns were compared with suitable non-comment evidence where available.
- The report names the sample, method, AI assistance, human review, and representation limits.
What a defensible result looks like
A useful finding connects a defined group of comments to a clearly bounded conclusion: “Among 86 survey comments collected through the help page during the stated period, 19 described difficulty locating the cancellation instructions. The comments were coded for task and issue separately; the 19 source IDs and exact excerpts were reviewed by an analyst. This channel does not show how common the problem is among all customers, and responses from other languages were limited.”
That form preserves context, exposes the counting unit, identifies the evidence, and states what the data cannot establish. It gives a service owner a basis for checking the page without turning an AI summary into an unsupported claim about every customer.
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