Yes. AI can help turn approved expert interviews into a searchable internal troubleshooting library. The essential step is converting each useful incident into a reviewed entry with equipment context, supporting evidence and clear limits. A transcript alone leaves staff to decide which parts of a conversation apply to their problem.
Build a small library around one equipment family or recurring support problem first. This lets you judge whether the interviews contain enough detail, whether reviewers can verify the advice, and whether staff can retrieve the right entry.
Decide which expert knowledge is worth capturing
Choose incidents where the expert can explain their reasoning: what staff observed, which checks narrowed the problem, what resolved it, and how they confirmed the outcome. “It usually needs adjusting” is a follow-up question, not a finished troubleshooting entry.
Ask the team to bring actual support questions to the interview. Separate symptom descriptions from assumed causes. “Labels stop feeding after a roll change” is a stronger starting point than “the sensor is faulty”. The first leaves room for several explanations; the second prejudges the diagnosis.
Select an expert who can review the resulting entries. If nobody can confirm the equipment details or remembered steps, the interview may still be useful background, but it should remain outside the approved troubleshooting collection.
The business decision is whether custom AI agents would help staff retrieve reviewed knowledge. Start by defining the records you need, before choosing the conversational interface.
Interview for a reusable incident, not a general lecture
Use a consistent sequence, while allowing the expert to explain exceptions:
- What did the person see, hear or read on the display?
- Which equipment model, software version and configuration were involved?
- What checks were performed, and what did each result mean?
- What action resolved that particular incident?
- How was normal operation confirmed?
- When would the same advice be inappropriate?
Ask what failed as well. An unsuccessful check can help another employee avoid repeating work, provided its conditions are recorded.
Capture the expert’s uncertainty in their own terms. “I think this was the older model” should remain an unresolved model detail. Ask a follow-up question or request a supporting record; do not silently convert it into a definite equipment identification.
Agree what may be recorded and shared internally before the interview. Remove unnecessary customer details and unrelated personal conversation from the material prepared for the library. Have the appropriate business owner decide recording permission, access and retention for your circumstances.
Transcribe the recording and verify consequential words
OpenAI’s speech-to-text documentation describes transcription of completed audio recordings, with specialised options for speaker labels and timestamps. These capabilities can support locating the relevant interview passage. Source: OpenAI speech-to-text
Keep the original recording and its transcript connected through a stable interview reference. Where timestamps are available, preserve the segment boundaries. Otherwise, use a transcript section reference and record that precise timing has not been captured.
Review words that change the action: model names, fault codes, units, sequence, and statements such as “do not restart”. Replay unclear audio rather than guessing from surrounding sentences. Mark inaudible content explicitly.
A short glossary of equipment names can provide transcription context. The documentation cautions that keyword hints should be evaluated for accuracy, including whether unspoken terms appear. Treat glossary support as an aid to checking, not proof that the transcript is right.
Before deployment, check current product availability and account, plan or region eligibility for the chosen setup. The supplied documentation establishes capabilities, not eligibility for your organisation.
Convert each incident into a reviewed entry
Ask AI to extract candidate records from the checked transcript. Each record should cover one recognisable problem in a defined equipment context. Leave missing fields visibly unresolved rather than completing them from general knowledge.
Structured Outputs can constrain extracted information to a supplied schema. That helps keep records consistent, but a correctly shaped record still needs its claims checked against the interview. Source: OpenAI Structured Outputs
Use separate descriptions for the observed symptom, suspected cause and confirmed resolution. If the expert only suggested a possible cause, label it as a possibility. Do not present it as the diagnosis.
The reviewer should check both accuracy and applicability. They may approve the entry, request clarification, narrow it to a particular configuration, or reject advice that cannot be supported. These are proposed workflow rules; the transcription and extraction tools do not provide this approval process automatically.
Reusable troubleshooting entry template
Copy this template for each candidate incident. Use “Unknown , clarification required” where evidence is missing. The publication checks below are proposed operating rules.
Entry ID and title: [Stable reference; symptom in staff language]
Search phrases: [Fault code, equipment terms and common symptom descriptions]
Equipment scope: [Make/model; software or firmware version; relevant configuration; excluded variants]
Observed problem: [What happened; operating conditions; information already checked]
Evidence and provenance: [Interview reference; speaker; recording date or “Unknown”; timestamp or transcript section; supporting passage]
Checks and interpretation: [Ordered checks described by the expert; what each result means; unresolved branches]
Reviewed resolution: [Supported action; prerequisites; who is authorised to perform it]
Confirmation: [Observable result that the reviewer accepts as evidence of resolution]
Limits and uncertainty: [Missing details; tentative explanations; conditions where advice does not apply]
Stop and refer: [Conditions requiring an appropriate technical specialist; named team or role]
Ownership and status: [Technical owner; reviewer; review date; draft / clarification required / approved / retired]
Duplicate or related records: [Existing entry references; reason to merge or keep separate]
Publication checks: [ ] Source checked. [ ] Equipment scope confirmed. [ ] Uncertainty preserved. [ ] Action reviewed. [ ] Staff access agreed. [ ] Retrieval tested.
Handle ordinary, incomplete and duplicate cases differently
The following scenarios, equipment names and identifiers are hypothetical. They illustrate handling rules, not tested instructions for real equipment.
Ordinary case: An expert describes a label printer, Model Cedar, that stopped feeding after a roll change. The interview identifies the configuration, the approved operator check and the observation confirming recovery. A reviewer verifies those details and approves an entry titled “Cedar: labels stop feeding after roll change”. A staff answer should show its scope and source, then present only the reviewed guidance.
Missing or ambiguous case: Another passage says, “Check the sensor on that older unit,” without identifying the unit or the check. The candidate record stays at “clarification required”. The reviewer asks which model was involved and what “check” meant. If the expert cannot confirm it, the library does not offer that passage as an actionable resolution. A staff search may instead explain that no approved entry covers the specified equipment and direct the question to the technical owner.
Duplicate case: Two interviews describe the same Cedar symptom and reviewed resolution. The owner compares equipment scope, conditions and confirmation criteria before merging them. Keep both source references on the consolidated record. If one incident concerns a different configuration, keep separate entries with explicit scope; similar wording does not establish equivalent instructions.
Make reviewed entries searchable and test what staff receive
OpenAI file search supports semantic and keyword search across uploaded files, and its responses can include file citations. Files must be added to the knowledge collection and reach the documented completed status before use. Source: OpenAI file search
For this proposed library, search approved entries and retain their source references. Design equipment selection, status restrictions and staff access deliberately; uploading files does not establish those business rules.
Test staff wording, exact fault codes, missing model details and near-matching equipment. Check that the answer preserves uncertainty and that its citation supports the actual guidance. Record wrong matches and the reviewer’s correction before widening access.
A custom AI agent could provide the question interface, while predictable processing steps handle transcription and review queues. The AI agents versus automation comparison helps distinguish those choices. For the wider design, see custom AI agent workflows.
FAQ: expert interviews and troubleshooting libraries
Should staff search the complete interview transcript?
Keep transcripts available to authorised reviewers as evidence. For routine troubleshooting, prefer approved entries with clear scope. A conversation can contain unfinished suggestions that were never intended as instructions.
What if the expert remembers the fix but cannot explain why it worked?
Record the observed outcome and uncertainty separately. Ask the reviewer whether the evidence supports any reusable guidance. A successful incident alone may be insufficient to approve a repeatable resolution.
How do we choose the first interviews?
Choose a narrow set of recurring questions with identifiable equipment and available reviewers. Include an incomplete incident and overlapping accounts so the pilot tests clarification and duplicate handling as well as straightforward extraction.
If your business needs help turning interview knowledge into reviewed, searchable entries, explore AI automation and get in touch to discuss a focused pilot.