Introduction
Choose whose language the glossary is meant to explain
An interview glossary can explain the participant’s language, the team’s preferred terminology, or the vocabulary a public reader needs. Those are related tasks, but they do not always produce the same entries. A researcher may want to retain a customer’s distinctive expression because it reveals how the customer understands a workflow. A product team may want a consistent display label so colleagues can compare related passages. A public guide may need an externally checked general definition that the interview never supplied. Decide which job you are doing before asking ChatGPT to produce a polished list. Otherwise, familiar dictionary meanings can quietly replace the local usage that made the interview useful. A good internal glossary preserves the participant’s observed wording and explains the scope of any editorial label. It should make the source easier to understand, rather than make the participant sound as though they already used the company’s preferred taxonomy or a standard industry definition.
Keep the reader’s purpose specific. If a designer needs to understand what a customer means by ready, a general dictionary entry for ready will be less useful than the customer’s condition for that status. If a researcher needs to compare two departments, a single merged entry can conceal an important difference between their labels. If a support writer needs approved public terminology, a research glossary can be an input to that work without becoming approval to publish every participant-specific phrase as product documentation. Identify the intended reader, the source set, and the kind of explanation needed. The same transcript can support several derivatives, but each should be labeled and reviewed for its purpose. The useful question is not how many terms ChatGPT can list. It is whether the resulting entries help this reader interpret the interview without acquiring meanings, certainty, or authority that the source did not provide.
References: GOV.UK: planning user research; GOV.UK: sharing user research findings.
Separate a word’s appearance from an explanation of its meaning
A term list records candidate expressions. A glossary entry adds a reviewed explanation and context. A definition claim therefore needs more than proof that a string occurred somewhere in the transcript. An abbreviation may be mentioned without expansion, and a familiar expansion may appear only because the speaker rejected it. A label may describe one workflow state without establishing how the product uses that label elsewhere. Ask ChatGPT to preserve these distinctions in the draft instead of completing every entry from general knowledge. A useful glossary can contain a short unresolved item when the interview does not supply enough information. That is often more valuable to a researcher than a fluent explanation with no source basis, because it identifies a specific question to clarify. The distinction should remain visible after polishing: a candidate term and an accepted source-based meaning are different editorial states, even when both occupy tidy rows in a working document.
Use a source boundary for each explanation. In the invented snippets used throughout this guide, Maya defines several report-workflow labels, while Oren uses some of the same language differently. These are fictional, product-agnostic examples written to expose editorial decisions, not real customer accounts or generated ChatGPT results. Their value lies in the contrast between an expression’s presence and the meaning the surrounding passage supports. For example, a speaker can say that QC means queue check here, not quality control. The common expansion is present in the text, but it is not the supported local meaning. A glossary that merely finds recognizable terms and fills in familiar explanations can therefore reverse the evidence while appearing helpful. Keep enough surrounding context to detect that reversal, and distinguish an exact quotation from an editor’s paraphrase of the participant’s explanation. Both can be useful when represented honestly and tied to recoverable passages.
References: GOV.UK: using in-depth interviews; GOV.UK: analysing a research session.
1. Define the Glossary’s Scope and Entry Shape
1.1 Name the audience, source set, and editorial purpose
Begin with the audience, the source set, and the intended use. State whether the glossary describes one participant, one interview, a selected set of sessions, or an approved synthesis. Keep the language context visible when expressions come from more than one language or from internal labels whose meaning depends on the speaker. Choose a review standard for accepting an explanation: an explicit participant definition, a contextual paraphrase with support, or a clearly marked question requiring clarification. Decide how outside knowledge will be handled before drafting, and preserve source locators that a reviewer can recover. These choices prevent a short internal research aid from turning into an apparently authoritative dictionary. They also make the request to ChatGPT more precise. A glossary for interpreting one conversation can be useful without making claims about the whole product, the entire customer base, or every possible meaning of the words that appear in it.
A practical scope statement could say that the document explains report-workflow language used by the supplied participants, with meanings limited to those accounts unless another source is identified. That statement leaves room for an editor to choose readable labels while preserving the observed terms. It also tells the reader why some entries remain unresolved. If the interview mentions handover without explaining it, the glossary can record that gap instead of importing a conventional definition and presenting it as the participant’s view. If the team later wants a general reference, create that derivative deliberately and verify the external definitions it uses. The research glossary remains useful evidence for that work, but it does not become a general reference merely because its prose is polished. Keep the purpose close enough to the source that a reviewer can tell what accepting an entry actually means and what questions still need to be asked.
References: GOV.UK: planning user research; UK Data Service: preparing data for deposit.
1.2 Keep the observed label, meaning, scope, and evidence separate
Use distinct fields or clearly distinguishable parts for the observed form, the display label, the proposed meaning, the scope, and the evidence. The observed form preserves what appeared in the reviewed source. The display label is the editor’s navigation choice. The proposed meaning explains what that source supports, while scope identifies whose usage or which situation it describes. Evidence provides the route back. These parts can differ legitimately. An editor might display green — review approval to distinguish Maya’s usage from Oren’s green — missing-field status. The added qualifier is useful, but it should not be presented as wording the participant actually used. Keep an entry’s unresolved questions or editorial notes separate from its accepted explanation as well. A working glossary becomes easier to review when a request to clarify a term cannot be mistaken for a definition already established by the interview.
W3C’s SKOS guidance provides a useful conceptual distinction between lexical labels and documentation such as definitions, scope notes, examples, and history notes. A small interview glossary does not need to implement that data model to benefit from keeping those roles separate. Use an entry shape suited to the team and retain the distinction in ordinary language. A headword alone is not a definition, an example alone may not explain the complete meaning, and a history note should not silently overwrite the form actually used in an earlier interview. For an internal working document, a compact set of clearly named parts may be sufficient. The purpose is to preserve editorial relationships, not to add technical machinery. A reviewer should be able to inspect the source phrase, the explanation proposed from it, and the limits attached to that explanation without guessing which part came from the participant and which part was supplied during glossary construction.
References: W3C: labels and documentation in SKOS; GOV.UK publishing guidance: clear structure.
1.3 Prepare a limited working copy through the approved route
Use an approved handling route for the interview material and the intended reader. Keep a limited working copy that retains necessary context without including unrelated identifying details. Consider the ChatGPT account controls, the sharing destination, and the traceable source separately. OpenAI’s current guidance distinguishes model-training choices from saved history and other account or workspace arrangements; no single setting should be treated as the project’s complete permission decision. For a glossary, distinctive internal labels can themselves reveal context even after a participant’s name is replaced, so review what the intended recipients actually need. The practical aim is to supply enough material for faithful interpretation through the approved workflow. It is not necessary to upload every project record merely because the glossary concerns a few terms, and a shorter approved excerpt should still include the passages needed to understand conditions, corrections, and speaker-specific usage.
Keep a scope record with the source copy so that later editors know which interviews and revisions the glossary covers. If names are replaced by participant identifiers, use those identifiers consistently in the evidence notes. Do not remove so much surrounding material that a local definition becomes unintelligible or a qualification disappears. In the invented example, knowing whether Maya or Oren supplied an explanation is essential because their uses of green differ. The same principle applies to actual projects where teams, roles, or time periods use familiar labels in different ways. Handle that context according to the approved research and sharing arrangements rather than assuming either complete removal or unrestricted disclosure is always correct. A source-scoped glossary depends on enough context to interpret an entry, and its reader should understand the limits of that context without being given unrelated material that has no role in the task.
References: OpenAI: data controls in ChatGPT; UK Data Service: anonymising text data.
2. Prepare the Source and Ask for Evidence-Linked Candidates
2.1 Check the terms before building definitions from them
Review the source wording, especially abbreviations, negated expansions, speaker attribution, and uncertainty that could change a term’s interpretation. A single transcription error can create a plausible but nonexistent acronym, and a missing not can reverse a participant’s explanation of a familiar phrase. Keep uncertain wording visible until it can be checked against the recording or another approved source. Do not let a polished glossary conceal unresolved recognition work. If the source includes an editor’s explanation of a label, distinguish that addition from the participant’s own words. A glossary can preserve an observed term faithfully while recording that its meaning is unknown. It can also preserve a correct explanation under a clearer display label, provided the source wording remains recoverable. Settle these relationships before asking a drafting tool to make the entries sound authoritative, concise, or consistent across the entire document.
When a transcript is needed, WhisperAITranscribe is an audio and video transcription tool that can provide a recording-to-text stage before glossary work in ChatGPT. Review the text and supply it through the available workflow; this guide does not establish a native integration, glossary export, or automatic validation of meaning. An existing checked transcript can enter the same process directly. Preserve its source relationship and editorial convention so the glossary reviewer knows what was actually said and what was normalized or added. If a term’s spelling matters, return to an appropriate source instead of relying on a familiar expansion suggested by context alone. The transcript is the evidential starting point for the glossary, not a guarantee that every expression in it has been defined. A later drafting pass can organize the material, but it cannot resolve an inaudible acronym merely by making one interpretation read smoothly.
References: W3C: transcribing audio; UK Data Service: transcription instructions.
2.2 Preserve locators, participants, and the surrounding explanation
Supply stable locators, consistent speaker labels, the source revision, enough surrounding context, and a coverage map when the input spans sections. In the invented snippets, S01 through S15 are assigned editorial identifiers, not recording timestamps. They help a reviewer recover passages without implying that a model created timing precision. Keep the term’s explanation with the passage that supplies it, including nearby exclusions and later corrections. A label can appear in one section and receive a definition elsewhere, so a glossary built from only the first occurrence may be incomplete. Text and document uploads depend on the ChatGPT plan, settings, and currently available route; a bounded pasted excerpt can also serve the task. Acceptance of a file does not establish that the final glossary has accounted for every relevant passage. Ask for explicit coverage and review it against what was supplied rather than assuming a complete-looking list proves complete interpretation.
For a long interview, keep section boundaries understandable and avoid separating an expression from the answer that explains it. If a participant first uses QC casually and later expands it, preserve both the observed form and the defining passage. If another participant says they do not know the abbreviation, keep that account separate rather than letting the earlier definition imply shared understanding. A source map supports this review because it shows what each entry relies on. It also helps when the transcript is corrected: an editor can revisit affected definitions without reinterpreting the entire document from memory. Do not ask ChatGPT to invent a timestamp for a term merely to make the glossary look traceable. A valid paragraph or turn locator is more useful than false precision. The reader needs a recoverable path to the evidence and enough context to judge the explanation, not an impressive-looking reference that cannot be checked against the actual source.
References: OpenAI: uploading files to ChatGPT; UK Data Service: preparing data for deposit.
2.3 Request candidates and meaning status before a finished glossary
Make the first request about candidate terms, meaning status, supporting passages, and review questions. Useful working statuses can distinguish an explicit explanation, a contextual interpretation needing review, and a mentioned expression whose meaning is not supplied. These are editorial categories for the task, not a claim that ChatGPT will classify every entry correctly. Ask for scope and observed variants before asking for a uniform polished definition. Keep a separate place for proposed external explanations when the reader needs them, and identify those as outside the interview rather than participant evidence. This sequence lets the reviewer reject an unsupported interpretation before it becomes the document’s authoritative-looking headword. A smaller set of supported entries and clear gaps can be more useful than a long glossary completed from general knowledge. The purpose of the first draft is to expose the relationships that need review, not to make every candidate look equally finished.
An original reusable request is: “Using only the supplied interview, propose glossary candidates for an internal research reader. Preserve the observed term, suggest a clear display label, and identify the speaker or context. Give a meaning only to the extent supported by the source, with passage IDs and consequential exclusions. Mark expressions that are mentioned but not explained. Keep different meanings separate, and combine variants only when the evidence supports the relationship. Do not guess acronym expansions or treat a negated explanation as the intended meaning. Separate editorial questions and outside definitions from source-based entries. First provide candidates for review; polish accepted entries afterward.” Adapt the request to the source and intended reader. It is a drafting instruction, not a guarantee of semantic accuracy. The examples in this guide are authored comparisons rather than ChatGPT outputs, and an actual response still needs review against the supplied material before its entries are accepted.
References: OpenAI: prompting best practices; GOV.UK: analysing a research session.
3. Write Meanings That Preserve the Participants’ Distinctions
3.1 Keep explicit definitions and their exclusions together
An explicit explanation is a strong starting point, but keep its exclusion and local scope with it. In the invented source, Maya says a report becomes ready only after the review lead signs it off, then specifies that ready does not mean sent. The supported entry therefore concerns review approval, not delivery. Another passage explains sent as delivery to the receiving team. Merging the two labels into a single status would contradict the interview rather than simplify it. A concise entry can preserve both the condition and the distinction: ready, in Maya’s workflow, means the required review sign-off is complete; it does not establish that the report has been sent. That paraphrase is an editorial explanation supported by the passages, not a sentence Maya spoke verbatim. Keep the observed wording and the paraphrase distinguishable so a later reader can recover both the term and the evidence for the explanation.
The same care applies to ordinary words such as owner. Maya defines the owner as the account lead for the report and explicitly excludes the person who drafted it. A familiar everyday reading of owner could therefore be unhelpful in this local workflow. Preserve the role distinction instead of selecting the definition that sounds most conventional. If the team chooses a display label such as report owner — account lead, retain owner as the observed term and identify the added qualifier as editorial. That helps readers navigate without rewriting the participant’s vocabulary. An entry is useful when it explains how the term works in this account and what the reader should avoid inferring from it. It does not need to settle every possible meaning of owner elsewhere. The source supplies a local boundary; the glossary should make that boundary easier to see rather than remove it in pursuit of a shorter or more general-sounding definition.
References: GOV.UK: using in-depth interviews; GOV.UK: analysing a research session.
3.2 Combine variants only when their relationship is supported
Keep observed variants separate from the editorial label chosen for navigation. Maya explicitly says that hold and on hold name the same paused state in her account, so the glossary can connect those forms within that scope. Oren says an old screen used queued and the newer notes use waiting for the same state in his usage. That supports a local relationship and a wording-history note. It does not establish that hold, queued, and waiting are all interchangeable across the product or every participant. Similar-looking workflow language can describe different steps. Ask ChatGPT to propose relationships with their evidence instead of merging candidates solely because they appear synonymous in general English. Preserve the forms readers will encounter in the interview, even if one form is selected as the display label. This lets the glossary help someone find a passage without flattening the distinctions the research is meant to uncover.
A preferred label can make a working glossary easier to read, while alternate forms keep source language recoverable. W3C’s SKOS primer distinguishes preferred and alternative lexical labels, including uses such as abbreviations and variants; that is a useful reference for separating label management from meaning review. It does not mean every near-synonym found by a model should be accepted as equivalent in this interview. Let the source determine the actual relationship. When a proposed merger lacks support, keep separate candidates or record a question to clarify with the appropriate person. A small glossary can tolerate some unresolved duplication better than a confident false merger. Once meanings are combined, later summaries or search tools may treat the combined entry as evidence of agreement. Preserve enough detail that an editor can see whether the relationship was stated by a participant, inferred for review, or adopted as a team navigation decision after the source was examined.
References: W3C: lexical labels in SKOS; UK Data Service: preparing data for deposit.
3.3 Keep the same label separate when the meanings differ
The same label can represent different meanings that deserve qualified entries. In the invented snippets, Oren says green means no fields are missing and does not establish completed review. Maya says green means the review lead has approved the report. A merged explanation saying green always means both complete fields and approved review would create a combined rule neither participant supplied. Keep the observed form green attached to each account, then use an editorial qualifier to distinguish the entries for readers. For example, green — missing-field status and green — review approval can serve as navigation labels when their source scopes remain visible. The qualifiers should not be presented as participant quotations. This is a useful analytical result, not an imperfection to clean away: the different uses may explain why people appear to agree on a label while acting on different assumptions about what that label establishes.
If the source text came from WhisperAITranscribe, preserve the speaker attribution and relevant explanation while the glossary is revised. A cleanup that makes both passages read more uniformly should not erase the distinction between the accounts. Nor should one participant’s uncertainty be overwritten by another participant’s definition. Oren’s statement that nobody explained QC to him is evidence about his understanding, even though Maya supplies a local expansion elsewhere. The team may later investigate whether the same interface or process is involved, but the glossary should not assume that conclusion. Keep the proposed relationship inspectable. If a shared concept is eventually accepted, record the evidence and the editorial decision that supports it. Until then, distinct scoped entries are often more honest and more useful than a single polished definition that hides disagreement, uncertainty, or the possibility that the same written label belongs to different parts of a workflow.
References: GOV.UK: analysing a research session; GOV.UK: sharing user research findings.
4. Review the Evidence Before Accepting an Entry
4.1 Check literal occurrence, negation, and definition status separately
A literal occurrence shows that an expression is present. It does not establish meaning evidence, and it does not interpret negation or determine an entry’s definition status. In the invented source, QC appears in a passage that expands it as queue check and rejects quality control as the local expansion. A draft that selects the common expansion would reverse the evidence while citing a passage that genuinely contains those words. QA presents a different problem: Maya mentions it near the end without explaining it. The source does not establish the familiar expansion a model might suggest. Handover is also mentioned without a definition. Keep those items as unresolved candidates or explicitly separate an externally checked general explanation if the reader needs one. Do not let familiarity make the distinction disappear. A glossary should show what the supplied account supports, even when general knowledge makes an unsupported completion sound obvious.
A source locator is useful only when the reviewer reads what it locates. The authored alternative QC means quality control can point at the correct passage ID and still be wrong. A check that merely confirms the ID exists cannot catch the reversal. Inspect the explanation’s wording, speaker, condition, exclusions, and scope. Then read nearby material for a correction or a different use. If the entry is paraphrased, check the meaning rather than requiring identical wording; if it is quoted, also check the actual words. An unexplained abbreviation can remain useful as a follow-up item without being promoted into an accepted definition. This keeps the glossary from becoming a collection of plausible expansions that are difficult to distinguish from the participant’s evidence. The more authoritative the display looks, the more important it is that the accepted meaning reflects the source rather than the reader’s expectation of what a familiar abbreviation normally means.
References: UK Data Service: transcription instructions; AP: accuracy and representation principles.
4.2 Use an occurrence index without treating it as interpretation
A local index for this guide searched fifteen invented passages for thirteen candidate labels. A naive substring search found ready in five passages, while the selected word-boundary search found it in four passages. The extra match was the word already in the sentence “The report is already sent.” A negative statement saying the report was not ready still matched the bounded index, correctly showing that the word occurred without establishing readiness. These are actual local text-matching observations, not ChatGPT results. A WhisperAITranscribe transcript could be indexed for review in a separate workflow, but this guide does not establish a native indexing feature or automatic semantic analysis. The example shows why an index can help locate evidence while remaining unable to decide the meaning of an entry, whether a status applies, or which candidate deserves a definition. Those decisions require reading the relevant passages.
The counts describe this deliberately constructed fixture and its particular matcher. They do not measure term importance, customer prevalence, or model accuracy. The index also distinguished the literal labels QA and QC, yet it could not determine whether either abbreviation had a supported expansion. Its word-boundary approach was chosen for these English and mixed-label snippets; it is not a universal segmentation method for every language or writing system. Use that boundary honestly. An actual research index should be reviewed for the source’s spelling, punctuation, and language conditions, then used as a navigation aid. A high occurrence count can reflect repeated interviewer wording, a common procedural phrase, or several uses with different meanings. A single well-explained local term may be more useful than a frequent unexplained abbreviation. Keep selection and acceptance grounded in the reader’s purpose and the evidence, rather than allowing an easily computed count to become an unexamined ranking of what the glossary should contain.
4.3 Separate source meanings, outside definitions, and translation notes
Review acronym expansion, participant scope, outside knowledge, and evidence quality as separate questions. A WhisperAITranscribe source transcript can provide the wording to inspect, while the glossary remains an editorial derivative. In the twelve authored alternatives for this guide, ready was wrongly merged with sent, QC was given an explicitly rejected expansion, QA received an unsupported expansion, green was falsely combined across accounts, handover was completed from a plausible outside interpretation, and owner was assigned the role the source rejected. These are designed comparison cases, not sampled ChatGPT outputs or a measured error rate. Their value is that each identifies a concrete repair: restore the local condition, remove the guessed expansion, retain distinct scopes, or mark the meaning as unresolved. A general instruction to be cautious is less useful than identifying exactly which part of a proposed definition exceeds or contradicts the source.
Multilingual labels need the same separation. In the invented example, Maya explicitly identifies listo as a Spanish label aligned with ready in this workflow and preserves the exclusion that it does not mean sent. The glossary can record that source-based local alignment without claiming it is the universal translation appropriate to every context. If an editor supplies another-language display label, mark that work separately and review it with the appropriate language knowledge. Keep the original observed form recoverable so that a later reader can find the actual passage. Translation, spelling normalization, and conceptual equivalence are different decisions. A readable glossary can explain them briefly without pretending the interview itself supplied every multilingual label. When an external definition or translation is added, verify the relevant source and identify the contribution. It should not silently acquire the authority of participant testimony simply because it appears beside an interview-derived term in the same document.
References: Reuters: standards and values; GOV.UK publishing guidance: clear language.
5. Deliver a Glossary That Remains Inspectable
5.1 Give the reader a concise entry and the reviewer a source route
A useful handoff can separate the reader-facing glossary, evidence notes, and open questions without making the reader inspect a production log. The entry should explain the term plainly and preserve any qualification needed for correct interpretation. The source note should identify the relevant passage and participant. The open question should remain an editorial task rather than part of an accepted definition. An illustrative entry might read: Ready — Maya’s review status. The report has received the review lead’s sign-off; delivery is not established by this status. The supporting passages are S01 and S02, and the explanation is a paraphrase. This is an authored example, not an exported glossary result. It gives the reader a usable explanation while leaving the reviewer a route to the original wording. Choose the amount of visible detail according to the audience, but keep the supporting relationship available through the approved source-handling route.
Do not hide a consequential limitation in a general note far from the entry it changes. If green has two meanings in the source set, the reader needs to see which one an entry describes. If QA is unexplained, an unresolved label is more honest than an invented expansion with a small disclaimer elsewhere. Keep display labels descriptive and use consistent entry parts so the reader can compare like with like. Consistency should help reveal differences, not erase them. If the glossary is forwarded, check that its source scope and editorial status travel with it. A local research aid can become misleading when copied into another document as though it were approved product terminology. If the team decides to create such a reference, identify that new task and review its claims. The interview glossary can inform the decision while remaining an account of language observed in the supplied material, with its own evidence and limits intact.
References: GOV.UK: sharing user research findings; GOV.UK publishing guidance: clear structure.
5.2 Expand across interviews without assuming a shared concept
When adding sessions, review concept identity and language alignment before merging entries. A common label can conceal different meanings, and different labels can describe the same local state when the source establishes that relationship. Keep participant-level explanations available while proposing a broader glossary. Do not convert a repeated word into proof that every interviewee understands the same concept. A term’s absence also does not prove that a participant lacks the underlying idea; the interview may simply have used other language or never addressed it. The source set and research purpose determine what can be inferred. If the team needs a shared taxonomy, treat that as an editorial synthesis with its own acceptance decisions, not an automatic consequence of pooling several transcripts. Preserve the differences that motivated the research so the final vocabulary does not manufacture agreement that the interviews were useful precisely because they called into question.
If several source transcripts were prepared with WhisperAITranscribe, retain their distinct revisions and participant identifiers when supplying them for ChatGPT drafting. Ask for proposed relationships with supporting passages before accepting a combined entry. Keep language-specific observed forms alongside any reviewed display labels, and distinguish a local translation supplied by a participant from one added later by an editor. The invented listo example shows one supported relationship; it does not establish a translation procedure for every multilingual interview. A source index may also need different treatment for different writing systems, spelling conventions, or token boundaries. Review those conditions instead of assuming an English-oriented matcher covers the whole corpus. The useful result is a glossary whose shared entries have an explainable basis and whose unresolved differences remain visible. Adding more material should improve the reader’s understanding, not merely increase the number of source references attached to an increasingly broad definition that no individual passage actually supports.
References: GOV.UK: planning user research; UK Data Service: preparing data for deposit.
5.3 Keep accepted meanings and later changes traceable
Retain an accepted source revision, an entry identifier, a change note, and a pending-review state where needed. These can be simple editorial records. If a WhisperAITranscribe transcript correction changes an abbreviation or restores a negation, revisit the affected entry and any glossary derivative that relied on it. A WhisperAITranscribe revision does not automatically update a separate ChatGPT draft, working document, or reference already shared with colleagues. Keep the entry’s identity distinct from its current wording so an ordinary label correction does not obscure which concept is being reviewed. If two meanings are split after review, preserve the reason rather than silently reusing one identifier for a different account. The aim is to let a later editor understand what changed and which evidence supports the current explanation, without having to reconstruct the entire drafting conversation or assume the newest wording must have appeared in the original interview.
An illustrative record could keep separate identifiers for green in Maya’s approval account and green in Oren’s missing-field account, even if both entries initially displayed the same short label. A later decision to use qualified labels would then change presentation without pretending the source wording changed. If a new interview explains a previously unresolved term, add that source explicitly rather than backfilling the earlier conversation as though it already contained the definition. A transcript prepared through WhisperAITranscribe can remain the record of that earlier account while the glossary becomes a synthesis across identified revisions. Preserve that distinction in the handoff. It makes an update more useful because readers can tell whether the change corrected recognition, clarified an editorial interpretation, or introduced new evidence. Those are different reasons for revising an entry, and the difference matters when the glossary is used to compare accounts or support a product discussion over time.
6. Conclusion
6.1 Let the evidence determine the meaning and its scope
A trustworthy interview glossary connects observed wording, supported meaning, and visible scope. WhisperAITranscribe can provide a recording-to-text starting point for review, while ChatGPT drafting, editorial acceptance, and source maintenance determine how the glossary is constructed and used. Keep the source expression separate from the navigation label, an explanation separate from an example, and a local meaning separate from a general definition. Preserve exclusions and uncertainty that change interpretation. Combine variants only when their relationship is supported, and retain distinct entries when the same label is used differently. A useful glossary does not need every candidate to have a finished definition. It needs the reader to understand what the supplied accounts establish and which terms still require clarification. That makes the document a dependable aid to interpreting the research rather than an authoritative-looking layer of familiar meanings added after the interview.
The invented snippets and authored alternatives make those boundaries concrete. Ready differed from sent because review approval did not establish delivery. QC had a local expansion that rejected a familiar alternative. QA and handover appeared without enough explanation to support a source-based definition. Green had different meanings in two accounts, while hold and on hold had an explicitly shared local use. A literal index located candidates but also showed why substring matches and negative statements need interpretation. These examples do not measure ChatGPT’s behavior on your material. They define practical questions for reviewing an actual draft: what does the passage explain, whose usage is being described, what is excluded, and which relationship was supplied by the editor? Answering those questions preserves the research value of the participants’ language while letting the glossary remain readable, concise, and open to correction when better evidence becomes available.
6.2 Use a repeatable sequence from transcript to accepted entries
Step 1: define the reader, source set, language context, and whether the glossary should describe local usage or include separately verified outside explanations. Step 2: establish the reviewed text, using WhisperAITranscribe when transcription is needed, and preserve speakers, corrections, and recoverable locators. Step 3: ask ChatGPT for candidate terms with proposed meaning status, scope, variants, and supporting passages before polishing accepted entries. Step 4: review definitions, exclusions, acronym expansions, merged concepts, and unresolved items, then deliver an identifiable version through the approved sharing route. Keep a correction path from each accepted entry to its evidence. If a term is unclear because the speech is uncertain, return to the recording. If the source is clear but supplies no definition, keep the gap visible or identify the external work needed. Another drafting pass cannot turn missing source evidence into a participant explanation.
Begin with a representative set of passages rather than only the easiest fully defined term. Include an abbreviation, a conditional explanation, and a candidate that is mentioned without being explained. Review the first proposed entries and adjust the request around observed problems. Then apply the accepted boundary to the remaining material while checking it for its own differences and corrections. If the glossary expands to more participants or languages, revisit scope and concept relationships rather than treating new labels as automatic synonyms. Keep the source map until the handoff has been reviewed, and identify later changes with the evidence that motivates them. The process stays manageable when each stage has a clear purpose: obtain the words, locate candidates, interpret the evidence, choose readable labels, and maintain accepted entries. A polished final list is useful because those relationships remain intact, not because its uniform appearance makes every definition seem equally established.
6.3 Preserve useful gaps instead of completing them by guesswork
The finished glossary should preserve meaning, attribution, uncertainty, and recoverable support. A checked WhisperAITranscribe transcript can supply the wording, and ChatGPT can help organize a draft, but an accepted definition still needs source-aware judgment. Keep unexplained terms visible as useful questions, distinguish local meanings from outside knowledge, and retain the participant’s expressions alongside any editorial display labels. Return to the recording and the WhisperAITranscribe text-review stage when the words need correction; return to the glossary interpretation when the issue concerns scope or an unsupported expansion. Preserve the difference between those repairs. The best outcome is a reference that saves the next reader time while leaving a dependable path to what the interview actually supports, including the limits and differences that a smoother but less faithful definition would have erased. That is how a glossary remains useful to the research instead of replacing the participants’ language with guesses.
References: W3C: transcripts; GOV.UK: sharing user research findings.