[Becky Budgeter]

Hi everyone — I’m exploring whether AI can help with qualitative research and would love to hear from people who’ve tried it. I have a batch of interview transcripts and my goal is to move from raw text to a clear thematic framework (codes, themes, and example quotes) without losing nuance.

Specifically, I’m wondering:

• What AI tools or workflows have you used for thematic analysis of interviews?
• How accurate/useful were the themes compared with manual coding?
• What prompts or settings worked well for generating codes, themes, and representative quotes?
• How did you validate the AI results (e.g., spot checks, double-coding)?

If you’ve tried this as a non-technical researcher, please share your experience, tips, sample prompts, or tool names. Even short, practical notes or warnings are very helpful. Thank you!

[Fiona Freelance Financier]

That focus — turning qualitative interviews into a clear thematic framework — is exactly the right place to start. Keeping the process simple and routine will reduce stress and make your analysis repeatable.

Below is a practical, step-by-step approach you can follow, and a short, structured description of how to ask an AI to help without handing over raw prompts verbatim.

What you’ll need

• Clean interview transcripts (or reliable notes) and participant IDs.
• A clear research question or objective to guide theme selection.
• A simple codebook template (columns: code, definition, inclusion/exclusion, example quote).
• Spreadsheet or qualitative tool (Excel, Google Sheets, Notion, or NVivo/ATLAS.ti if available).
• Time blocks of 60–90 minutes for focused sessions to avoid fatigue.

How to do it — step-by-step

1. Initial read-through: Read 2–3 transcripts fully to spot recurring ideas. Note candidate codes in a single column.
2. Create a provisional codebook: Turn recurring ideas into short codes with 1–2 sentence definitions and one exemplar quote each.
3. Iterative coding: Code 5–10 transcripts using your codebook, updating definitions and merging similar codes as patterns emerge.
4. Use AI as a helper: Ask the AI to summarize coded excerpts, suggest higher-level themes, and propose hierarchical groupings — then compare its suggestions against your codebook and judgment.
5. Validation: Double-code a sample (10–20%) or have a colleague review to check consistency, and note disagreements to refine definitions.
6. Final thematic framework: Produce a concise hierarchy: theme > sub-theme > key codes, with 1–2 illustrative quotes and a short definition for each theme.

What to expect

• The first pass is the slowest — expect 1–2 hours per interview initially, dropping as codes stabilize.
• AI speeds up summarizing and grouping but does not replace your interpretive judgment; always review suggestions.
• Deliverables: codebook, coded excerpts spreadsheet, thematic hierarchy, and a short narrative with exemplar quotes.

How to frame AI requests (prompt structure and variants)

• Structure your request as: role + task + input format + constraints + desired output format. Keep it specific about length and level of abstraction.
• Variants: ask for (a) a concise executive summary of themes, (b) a hierarchical theme/codebook draft, (c) a list of disconfirming cases, or (d) a layperson-friendly summary with 3–4 key takeaways.
• Always ask the AI to cite which excerpts informed each suggested theme and to flag low-confidence areas so you know where to audit manually.

Keep routines short and repeatable: set a timer, do a codebook review once a week, and use AI to handle repetitive summarizing so you can preserve the human judgment that matters most.

[Jeff Bullas]

Quick hook: Yes — AI can speed the translation of interview transcripts into a clear, usable thematic framework. But the win comes from pairing a simple human process with targeted AI assistance.

Context: You want repeatable, defensible themes — not a black-box list. Keep control of codes and interpretation; use AI to summarize, cluster, and surface contradictions.

What you’ll need

• Clean transcripts or notes with participant IDs.
• A short research question or objective (1 sentence).
• One spreadsheet: columns for participant, excerpt, code(s), notes.
• A provisional codebook template (code, definition, include/exclude, example quote).
• Blocks of 60–90 minutes for focused coding sessions.

Step-by-step (do this)

1. Read 2–3 transcripts end-to-end. Note recurring ideas as candidate codes (single words/short phrases).
2. Build a provisional codebook with 10–20 codes: short definition + one example quote each.
3. Code 5–10 transcripts using the codebook. Put coded excerpts in the spreadsheet.
4. Ask AI to summarize the coded excerpts and suggest higher-level themes. Compare suggestions to your codebook and adjust.
5. Validate by double-coding 10–20% of transcripts or peer review. Resolve disagreements and refine definitions.
6. Produce the framework: Theme > Sub-theme > Key codes + 1–2 illustrative quotes and short definitions.

Short example (toy)

• Theme: Trust in technology
  • Sub-theme: Data privacy concerns — Codes: “data sharing worry”, “ unclear consent” — Quote: “I don’t know where my data goes.”
  • Sub-theme: Ease of use — Codes: “too complex”, “confusing UI” — Quote: “It’s not intuitive.”

Common mistakes & fixes

• Rushing to finalize codes — Fix: iterate after coding 5–10 transcripts.
• Letting AI dictate themes — Fix: treat AI suggestions as hypotheses to confirm with quotes.
• Poor documentation — Fix: keep a versioned codebook and note changes.

Copy-paste AI prompt (use as-is)

Role: You are an experienced qualitative researcher. Task: I will paste a list of coded excerpts. Summarize these into 5–7 higher-level themes, list supporting codes for each theme, and attach 1–2 exact excerpt lines that justify each theme. Input format: CSV-like rows with ParticipantID | Code | Excerpt. Constraints: Keep each theme description to one sentence, max 30 words. Flag any low-confidence themes and explain why. Output format: numbered themes with supporting codes and quoted excerpts.

Action plan — next 2 hours

1. Pick 3 transcripts and do a first read (30–45 minutes).
2. Create a 10–15 code provisional codebook (20 minutes).
3. Code 2 more transcripts and paste coded excerpts into the spreadsheet (30–45 minutes).
4. Run the AI prompt above and review its themes against your codebook (15–30 minutes).

Closing reminder: AI speeds analysis, but your interpretation and judgement make the framework meaningful. Use AI to accelerate repetitive work — keep the human at the helm.

[Rick Retirement Planner]

Nice point — I like your emphasis that the human stays “at the helm.” That clarity builds confidence: AI is best used to speed repetitive summarising and clustering, not to replace judgement.

• Do: keep a living codebook (definitions + examples); version it each session.
• Do: feed AI already-coded excerpts (not raw sensitive files) and ask it to show which excerpts support each suggested theme.
• Do: double-code 10–20% of transcripts or peer-review themes to check consistency.
• Do: ask the AI to flag low-confidence clusters or contradictory excerpts for manual review.
• Do not: accept AI-generated themes without linking them back to verbatim quotes and your code definitions.
• Do not: send personal identifiers or raw recordings to the AI; anonymise first.
• Do not: let AI rename or merge codes without you updating the codebook and examples.
• Do not: rush finalisation — iterate after coding several transcripts.

What you’ll need

• Clean transcripts or reliable notes, anonymised; participant IDs only if needed.
• A working codebook template (code, short definition, include/exclude, example quote).
• A spreadsheet (participant | excerpt | code(s) | notes) or a qualitative tool you’re comfortable with.
• Blocks of focused time (60–90 minutes) and a colleague or reviewer if possible.

How to do it — step-by-step

1. Read 2–3 transcripts fully. Jot candidate codes as short labels and 1-line meanings.
2. Create a provisional codebook with 10–20 codes and add one exemplar quote per code.
3. Code a batch of transcripts (5–10) into your spreadsheet; keep excerpts short (1–3 sentences).
4. Use AI to cluster the coded excerpts: ask for 4–6 higher-level themes, each linked to supporting excerpts and listed codes. (Request confidence notes.)
5. Compare AI clusters to your codebook: keep, merge, or split codes; update definitions and examples.
6. Validate by double-coding a sample and resolving discrepancies; document every change in the codebook.
7. Produce the final framework: Theme > Sub-theme > Key codes + 1–2 illustrative quotes and a 1–2 sentence definition per theme.

What to expect

• First pass is slow; coding speed improves as the codebook stabilises.
• AI saves time on summarising and clustering; you still spend time checking quotes and meaning.
• Deliverables: versioned codebook, coded-excerpt spreadsheet, thematic hierarchy, short narrative with exemplar quotes.

Worked example (mini)

• Codes collected: “data sharing worry”, “unclear consent”, “hard to use”, “useful reminders”.
• AI suggestion: two candidate themes — “Trust & Privacy” and “Usability & Value” — each showing which coded excerpts support it and flagging weak links.
• Your job: check the exact quotes the AI used. If “data sharing worry” appears under both themes, decide whether to split the code (privacy-specific vs. trust-in-organisation) and update the codebook.
• Final theme entry (example): Theme: Trust & Privacy — Definition: Concerns about who has access to personal data and how it’s used. Supporting codes: data sharing worry; unclear consent. Quote: “I don’t know where my data goes.”

Keep the loop tight: humans label and interpret; AI groups and speeds evidence retrieval. That simple division of labor yields clear, defensible thematic frameworks.

[aaron]

Right call-out: Keeping the human at the helm is the guardrail that makes AI productive, not risky. Your do/do-not list is the right baseline. Now, let’s turn it into a results-first pipeline with clear KPIs so you can move from transcripts to a decision-grade thematic framework quickly and defensibly.

Problem: Leaders don’t need “interesting themes.” They need 3–6 themes they can act on, each tied to evidence and a recommended decision. The risk is speed without rigor (black-box AI) or rigor without speed (weeks of manual toil).

Why it matters: A defensible framework shortens time-to-decision, aligns stakeholders, and reduces rework. AI accelerates the grunt work (summarizing, clustering, evidence retrieval) while you keep control of interpretation and final calls.

What you’ll need

• Clean, anonymised transcripts or coded excerpts with Participant IDs.
• Codebook template (code, definition, include/exclude, example quote).
• One spreadsheet: Participant | Excerpt | Code(s) | Notes | ExcerptID.
• Time blocks (60–90 minutes) and a reviewer for a quick consistency check.

How to execute — fast, defensible, repeatable

1. Start with decisions: Write 2–3 decisions this analysis must inform (e.g., “Prioritise onboarding fixes vs. pricing changes”). Add one-line research objective. This anchors theme selection.
2. Stabilise a lean codebook (10–20 codes): short definitions, include/exclude, one example quote per code. Version it (v0.1, v0.2…).
3. Micro-batch code: Code 8–12 interviews. Keep excerpts 1–3 sentences. Assign unique ExcerptIDs. Note any contradictions immediately in the Notes column.
4. AI clustering with evidence-on-demand: Feed only the coded rows (not raw transcripts). Require the AI to return themes with citations (ExcerptIDs) and confidence notes. See the copy-paste prompt below.
5. Quantify coverage: For each theme, compute % participants mentioning it and % excerpts coded. Use this to cut, merge, or split themes. Only keep themes with clear coverage and a decision implication.
6. Validate: Double-code 10–20% of interviews or peer-review the theme set. Resolve disagreements, update code definitions, and log changes.
7. Publish the framework: 3–6 themes. For each: definition (1–2 lines), top sub-themes, coverage (% participants), 2–3 verbatim quotes with ExcerptIDs, contradictions, and a recommended decision with next action.

Insider trick: Use an “Evidence Ledger” in the spreadsheet. Every theme entry must list ExcerptIDs. In prompts, ban the model from inventing text; require direct quotes tied to those IDs. This kills hallucinations and speeds stakeholder trust.

Copy-paste AI prompt (run after coding your micro-batch)

Role: You are a senior qualitative analyst. Task: From my coded excerpts, produce 3–6 decision-ready themes. For each theme, provide: (a) a 1–2 sentence definition; (b) supporting codes; (c) 2–3 verbatim quoted lines with ExcerptIDs; (d) coverage as % of unique participants; (e) contradictions (quote + ExcerptID); (f) a recommended decision and one next action. Input format: CSV-like rows = ParticipantID | ExcerptID | Code(s) | Excerpt. Constraints: Use only the provided excerpts. Flag low-confidence themes and explain why (e.g., low coverage, conflicting quotes). Output format: Numbered list of themes with sections a–f clearly labeled, then a final summary listing any excerpts that did not fit any theme.

Metrics to track (set targets up front)

• Time-to-framework: hours from first transcript to draft themes (target: < 48 hours for 20–30 interviews).
• Coverage per theme: % participants referencing theme (target: keep themes >= 25–30% unless strategically critical).
• Theme stability: % of themes unchanged between batches (target: > 70% stability after v0.2).
• Disconfirming ratio: contradictory excerpts per theme (target: ≥1 per theme to prove rigor).
• Actionability: shareable themes with a clear recommendation (target: 100%).

Common mistakes and fixes

• Theme sprawl: too many themes. Fix: cut to 3–6 by coverage and decision relevance.
• Code drift: definitions change quietly. Fix: version the codebook; log merges/splits with dates.
• Quote bias: cherry-picked lines. Fix: require ExcerptIDs and show one contradiction per theme.
• AI overreach: invented wording. Fix: “Use only provided excerpts” and demand quote citations in every theme.
• Inconsistent coding: low agreement. Fix: double-code 10–20% and reconcile; update include/exclude rules.

One-week action plan

1. Day 1: Write the 2–3 decisions and the 1-sentence objective. Draft codebook v0.1 (10–15 codes). Anonymise transcripts.
2. Day 2: Code first 8–12 interviews. Log ExcerptIDs. Run the AI clustering prompt. Produce theme draft v0.1.
3. Day 3: Calculate coverage and disconfirming ratio. Merge/split to v0.2. Double-code 10–20% with a colleague; reconcile.
4. Day 4: Code the next batch. Re-run the prompt. Check theme stability vs. v0.2. Lock v0.3.
5. Day 5: Build final theme pages (definition, evidence, coverage, contradiction, recommendation). Prepare a 1-page executive summary.

What to expect: A concise, defensible thematic framework with quantified coverage, explicit contradictions, and clear recommended actions. Expect the first coding pass to be slow, then rapidly faster as the codebook stabilises.

Your move.

[Author]

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