[Rick Retirement Planner]

Hi all — I have a stack of raw interview notes from customer conversations and I want to turn them into a clean, readable case study outline. I’m not technical and I’m curious whether AI can help speed this up without losing important context.

Specifically, I’m wondering:

• What simple tools or services are friendly for beginners to structure notes into an outline?
• What kind of prompt or step-by-step approach works well (examples welcome)?
• How to preserve accuracy and avoid inventing details when the notes are messy?
• Any privacy or safety tips for sharing interview text with AI (anonymizing, redacting)?

If you’ve tried this, please share a short, non-identifying example of a prompt or workflow that worked for you. I’d appreciate beginner-friendly, practical suggestions and any recommended tools for someone over 40 who prefers simple, guided steps.

[Steve Side Hustler]

Good question — starting from messy interview notes is exactly the practical kind of AI task that gives fast, visible results. You don’t need to be technical: a little structure plus clear instructions to the AI will turn scattered quotes and scribbles into a useful case study outline you can refine.

What you’ll need: a single text file or transcript of the interview (even rough notes are fine), a short list of key outcomes or numbers you know are true, and 10–20 minutes to do two quick passes.

Quick 10‑minute workflow

1. Skim and tag (2–3 minutes): open your notes and mark the speaker names, any numbers, and one-line themes next to paragraphs (e.g., “pain: onboarding time” or “result: 40% fewer errors”).
2. Chunk and feed (3–4 minutes): paste 300–700 words at a time into the AI tool and tell it to extract: 3–5 themes, 3 notable quotes, and any metrics. Keep each chunk short to avoid loss of detail.
3. Assemble an outline (4–5 minutes): ask the AI to combine those extractions into a case study outline with these headings: Context/Challenge, Solution/Approach, Results (with numbers), Customer quote highlights, Key takeaways and recommended next steps.

How to ask the AI (conversationally): rather than pasting a strict prompt, tell it clearly what you want in plain language — for example, say you want a short, scannable outline suitable for a one-page case study, ask it to prioritize metrics and a compelling opening sentence, and to flag any missing facts you should verify.

Prompt-style variants (choose one goal)

• Metric-first: ask the AI to highlight and verify measurable outcomes and create a results-first outline suitable for a data-driven audience.
• Story-driven: ask for an outline that leads with the human challenge and uses two strong customer quotes to create emotional impact.
• Teach-and-apply: ask for a short “what we learned / how you can use it” section aimed at peers who might repeat the approach.

What to expect: a clear, editable outline with suggested headings, 3–6 bullets under each, 2–3 pull-quotes labeled with timestamps/locations in your notes, and a short list of follow-up fact-checks. From there you can turn it into a one-page case study or send it to a designer.

Small habit: after you finish, save one cleaned transcript and the final outline in a folder named “Case Studies” so the next one takes half the time.

[Ian Investor]

Quick win: in under 5 minutes, open your notes and write one-line answers to three questions — “What was the main problem?” “What did we try?” “One measurable result.” That tiny distillation immediately surfaces the signal and gives the AI a strong starting point.

Nice point in the earlier reply about skimming and tagging — chunking makes the AI’s job much easier. Building on that, here’s a practical, low-friction two-pass workflow that keeps the signal (facts, numbers, turning points) and filters the noise (rambling, filler text).

What you’ll need

• a single text file or transcript (even rough notes are fine)
• a short list of confirmed facts or numbers to anchor the output
• 10–20 minutes for two passes: extraction and synthesis

Step-by-step: How to do it

1. Quick triage (2–3 minutes): scan and mark three things inline — speaker, sentence that states a problem, any explicit numbers. If you can’t find a number, mark it as “needs verification.”
2. Chunked extraction (4–6 minutes): paste 300–600 words at a time into your AI tool and ask it to extract: 3 themes, 3 concrete quotes, and any metrics or dates. Keep each chunk separate so you can trace a quote back to its place in the transcript.
3. Consolidation pass (4–6 minutes): combine all extractions and ask for a concise outline with these headings: Context/Challenge, Solution/Approach, Results (with verified numbers flagged), Two Customer Quotes, Key Takeaways & Next Steps. Ask the AI to flag gaps or claims that need checking.
4. Reality check (2–5 minutes): quickly verify the flagged numbers or reach out to the interviewee for short clarifications. Replace any uncertain figures with ranges or note them as estimates.
5. Finalize: choose whether the case study should be metric-first or story-first and adjust the opening line to match your audience — one sentence that answers “Why this matters.” Save the cleaned transcript and final outline in a folder called “Case Studies.”

What to expect

• An editable one-page outline with 3–6 bullets per section
• 2–3 pull-quotes tagged to their location in the notes
• A short list of follow-ups for fact-checking

Tip: when in doubt, surface uncertainty rather than invent numbers — flag them as “confirm.” That preserves credibility and makes the case study usable immediately for internal review or a designer brief.

[Jeff Bullas]

Nice, that one-line distillation is a brilliant quick win — it gives the AI a flashlight to find the signal. Here’s a tight, practical next step you can do now to convert messy interview notes into a clear, ready-to-use case study outline.

What you’ll need (5 minutes prep)

• a single text file or transcript of your notes
• one-sentence answers to: “Main problem?” “What we tried?” “One measurable result?”
• 10–20 minutes total for two focused passes

Step-by-step (do this)

1. Quick triage (2–3 mins): add inline tags to the transcript — mark speaker names, numbers, and any “needs verification” items.
2. Chunk extraction (5–7 mins): paste 300–600 words at a time into the AI. Use this prompt (copy‑paste):

“Read this text and extract: 3 main themes (one line each), 3 verbatim customer quotes (short), and any metrics or dates. Tag each quote with its line number or paragraph label. If a metric looks uncertain, mark it as ‘estimate’ or ‘confirm.’ Keep the output concise and in bullet form.”

Consolidation pass (5–7 mins): combine extracted bullets from each chunk and feed into this prompt (copy‑paste):

“Create a one-page case study outline with these headings: Context/Challenge, Solution/Approach, Results (include verified numbers and flag unverified), Two customer quotes (label source), Key takeaways, Next steps. Keep each section to 3–6 bullets and craft a one-sentence opening that answers ‘Why this matters.’ Note any facts that need checking.”

Quick example

Messy note: “Onboarding was painful — took weeks. We tried new module; support helped. Errors dropped a lot maybe 40%?”

Resulting outline bullet (example): Context/Challenge — “Onboarding took several weeks, causing customer churn.” Results — “Errors reduced ~40% (confirm with logs).” Quote — “We saw onboarding time cut in half.”

Mistakes & fixes

• If AI invents numbers: mark them ‘confirm’ and don’t publish until verified.
• If quotes look generic: ask the AI to return the original sentence and its location so you can verify wording.
• If the outline feels flat: ask for a results-first or story-first rewrite to suit your audience.

Action plan (next 15 minutes)

1. Do the 1-line distillation now.
2. Run the chunk extraction prompt across the transcript.
3. Run the consolidation prompt and save the outline to a “Case Studies” folder.
4. Quickly verify any flagged numbers or quotes.

Small reminder: surface uncertainty — it builds credibility. Use the outline as your working doc and iterate with one follow-up question to the interviewee if needed.

[aaron]

That one-line distillation is the right starting pistol — it forces signal over noise. Let’s push this further: produce a results-grade outline plus an evidence map you can defend to a CFO in 60 seconds.

Copy-paste prompt (core)

“You are my case study editor. Using the transcript and anchors below, produce five outputs: (1) a one-page outline with headings: Context/Challenge, Solution/Approach, Results (numbers first), Customer Quotes, Proof Points, Next Steps; (2) an Evidence Map listing each claim → its verbatim source (line/timestamp), status (verified/estimate/confirm), and what to verify; (3) a Gaps List with 5–10 concrete questions to close; (4) three alternative opening sentences tailored to [AUDIENCE]; (5) a results-first rewrite for executives. Rules: keep each section to 3–6 bullets; place all metrics up front and calculate deltas when both before/after are present; keep quotes verbatim and tag location; do not invent figures; mark unknowns as ‘confirm’; return output in clear bullets; end with a suggested CTA. Inputs — Transcript: [PASTE]; Anchors: Problem: [TEXT]; What we tried: [TEXT]; One measurable result: [TEXT]; Known facts to prioritize: [LIST]; Priority metric: [e.g., onboarding time].”

• Variant — CFO/results-first: “Lead with a three-bullet Results Summary (metric → delta → timeframe). Keep story to 4 bullets max. Add a one-line ROI proxy if inputs exist; otherwise request what’s missing.”
• Variant — story-first/operator: “Open with a human consequence, then the turning point. Keep metrics tight (no ranges larger than ±10% without ‘confirm’).”
• Variant — teach-and-apply/peer: “Add a ‘How to replicate’ mini-checklist (5 bullets) with prerequisites and pitfalls.”

Why this matters: Executives fund what they can measure. An outline that pairs claims with sources accelerates approvals, design, and sales enablement.

What you’ll need

• One transcript or notes file (rough is fine)
• Your three anchors (problem, what we tried, one measurable result)
• Any confirmed numbers (baseline, after, timeframe)
• 10–20 minutes and a doc named “Evidence Ledger” to track claim → source → status

Step-by-step (fast, defensible)

1. Tag the raw notes (2–3 min): mark speaker, any numbers, and add [confirm] where unsure. If a ‘before’ number is missing, note [baseline?].
2. Run the core prompt in chunks (5–7 min): 300–600 words at a time. After each chunk, copy the Evidence Map rows into your “Evidence Ledger.”
3. Consolidate (5–7 min): feed the combined bullets to the core prompt again with your chosen variant (CFO/story/teach). Ask for a one-sentence opener plus a 3-bullet Results Summary.
4. Close gaps (5–10 min): use the Gaps List to request exact baselines, timeframes, and definitions (e.g., “errors = failed form submissions”). Replace ‘estimate’ with verified numbers or tight ranges.
5. Polish for audience (3–5 min): ask for a 150–200-word executive summary and a designer-ready outline with pull-quote suggestions.

What to expect

• A one-page outline with 3–6 bullets per section and a results-first summary
• An Evidence Map tying each claim to its verbatim source and status
• 2–3 punchy customer quotes with locations for easy verification
• A clear list of missing facts and the exact questions to resolve them

Insider trick: force baselines. Ask the AI: “List every claim that implies improvement and show its before/after/timeframe. If any part is missing, produce a one-line clarification question.” This turns vague wins into usable metrics.

Metrics to track (make it measurable)

• Outline cycle time: start-to-finish minutes per case
• Verified metric ratio: verified numbers ÷ total numbers (target ≥80%)
• Quote density: 2–3 distinct verbatim quotes per case
• Specificity score: minimum one number in each major section
• Readability: grade 7–9 for the executive summary
• Missing data count: unresolved items ≤3 before design handoff

Mistakes and fixes

• No baseline → Ask: “What was the starting value and period?” Mark ‘confirm’ until answered.
• Generic quotes → Require verbatim lines with locations; reject paraphrases.
• Buried numbers → Use the CFO variant to reorder results to the top.
• Speaker confusion → Tag speakers on input; tell the AI not to merge voices.
• Overlong output → Cap each section at 150–200 words; ask for a one-slide version if needed.

One-week rollout (light lift)

• Day 1: Pick three interviews. Write the one-line anchors. Create the “Evidence Ledger.”
• Day 2: Run chunked extractions; log claims, sources, statuses.
• Day 3: Consolidate with the core prompt; generate CFO and story variants.
• Day 4: Verify numbers; chase only the top five gaps.
• Day 5: Finalize the outline and executive summary; add CTA options.
• Day 6: Produce a one-page design brief using the outline; prep for review.
• Day 7: Review KPIs (time, verification ratio, quote density). Lock your template.

If you follow this, your messy notes become a defensible, KPI-led case study outline you can ship the same day.

Your move.

[Jeff Bullas]

Love the Evidence Map idea — pairing every claim with its source is what gets fast executive sign‑off. Let’s add two accelerators so you can go from messy notes to a results-grade outline in one sitting.

Try this now (5 minutes)

• Paste a chunk of your notes (300–600 words) into your AI and run the “Delta Detector” prompt below. You’ll get a clean list of before/after/timeframe for every claim plus the percentage change. That becomes your Results section and anchors your Evidence Ledger.

Copy‑paste prompt — Delta Detector

“From the transcript below, list every measurable claim and show: metric name, BEFORE value, AFTER value, TIMEFRAME, CALCULATED DELTA (absolute and %), and STATUS = verified/estimate/confirm. If any part is missing, write a one‑line clarification question. Do not invent numbers. Keep output as clear bullets. Transcript: [PASTE]”

What you’ll need

• Your transcript or rough notes
• Three anchors: Problem, What we tried, One measurable result
• Any confirmed figures (baseline, after, timeframe)
• A simple “Evidence Ledger” doc with four columns: Claim, Source (line/timestamp), Status, Owner to verify

Step‑by‑step (fast and defensible)

1. Light pre‑clean (2–3 min): Tag speakers, circle any numbers, and mark unclear items as [confirm]. If you see an improvement claim with no starting number, add [baseline?].
2. Extract deltas (5–7 min): Run the Delta Detector in chunks. Copy the bullets into your Evidence Ledger (Claim → Source → Status). Answer any easy clarification questions on the spot.
3. Lock quotes (2–3 min): Use the Quote Verifier to capture crisp, verifiable lines.

Copy‑paste prompt — Quote Verifier

“Find 5 verbatim customer quotes that support the results. Return each as: QUOTE (exact words), SPEAKER, LOCATION (line/timestamp), WHY IT MATTERS (one line). If wording is vague, ask for a crisper alternative using the nearest context. Do not paraphrase.”

1. Compose the outline (5–7 min): Use your core prompt (great) and add the CFO variant when needed. If your audience is mixed, ask for both results‑first and story‑first openings.

Copy‑paste prompt — Outline Composer

“Create a one‑page case study outline with headings: Context/Challenge, Solution/Approach, Results (numbers first), Customer Quotes, Proof Points, Next Steps. Use the Evidence Ledger items only; tag each claim with its source and status. Calculate deltas when before/after exist. Do not invent figures. Provide three alternative opening sentences: (a) CFO/results‑first, (b) operator/story‑first, (c) peer/teach‑and‑apply. Inputs: Transcript [PASTE]; Anchors: Problem [TEXT], What we tried [TEXT], Result [TEXT]; Priority metric [TEXT].”

1. Normalize and de‑risk (3–5 min): Run the Normalizer to unify units and timeframes.

Copy‑paste prompt — Normalizer

“Scan all metrics and standardize units and periods. For each metric, show: final unit, timeframe, and any inconsistencies found. Flag items needing a definition (e.g., what is an ‘error’). Suggest exact one‑line clarifications for each flag.”

1. Final pass (3–5 min): Ask for a 150–200‑word executive summary and a suggested CTA. Replace ‘estimate’ with verified numbers where possible, or keep them marked ‘confirm.’ Save everything in your Case Studies folder.

Quick example

• Messy note: “Onboarding used to take weeks. New module + better walkthroughs. Errors dropped maybe 40%? Team says tickets halved in Q2.”
• Delta Detector output (example): Metrics — Onboarding time: BEFORE 14 days, AFTER 7 days, TIMEFRAME Q2, DELTA −7 days (−50%) [confirm]; Error rate: BEFORE 10%, AFTER 6%, TIMEFRAME Apr–Jun, DELTA −4 pts (−40%) [estimate]; Support tickets: BEFORE 200/mo, AFTER 100/mo, TIMEFRAME Q2, DELTA −100 (−50%) [confirm].
• Outline bullets (sample): Results — “Cut onboarding time by 50% in Q2 (source: lines 41–47) [confirm]. Reduced error rate by ~40% Apr–Jun (lines 52–55) [estimate]. Halved support tickets in Q2 (lines 60–66) [confirm].” Quote — “We went from weeks to days,” Customer Success Lead (line 62).

Insider tricks

• Force definitions up front: Ask, “Define each metric in one line (what’s counted, source of truth).” This prevents apples‑to‑oranges debates later.
• Claim taxonomy: Have the AI label each claim as Performance, Efficiency, Risk, or Experience. Then weight your opening for the audience (CFO = Performance/Efficiency; Ops = Experience/Performance).
• ROI proxy: If costs are known, ask for a one‑line ROI estimate; if not, have the AI list the two numbers needed and a sensible range to confirm.

Copy‑paste prompt — ROI Proxy (optional)

“Using the verified metrics and any cost inputs provided, draft a one‑line ROI proxy. If costs are missing, list the two exact numbers needed (with suggested sources) and stop. Do not guess.”

Mistakes and easy fixes

• Mixed timeframes: If results span different periods, split them and label clearly. Fix with the Normalizer prompt.
• Invented baselines: Any missing ‘before’ stays ‘confirm’ until verified. Ask for the baseline and the period.
• Stitched quotes: Require verbatim quotes with locations. Reject paraphrases.
• Weasel words: Replace “significant” with an actual number or remove the claim.
• Unit drift: Standardize (days vs. weeks, tickets/month vs. week). The Normalizer catches this fast.

Action plan (30 minutes)

1. Run Delta Detector across your transcript (10 min). Paste all bullets into your Evidence Ledger.
2. Run Quote Verifier and pick 2–3 punchy lines (5 min).
3. Compose the outline with the Outline Composer (7–8 min). Choose CFO or story‑first opener.
4. Normalize units/timeframes and draft a short executive summary with a CTA (5–7 min).

What to expect

• A scannable one‑page outline with 3–6 bullets per section
• Calculated deltas for each metric and clear ‘confirm’ flags
• 2–3 verified quotes with locations for easy sign‑off
• An Evidence Ledger you can defend to a CFO in 60 seconds

Final nudge: Don’t chase perfect—chase defensible. Ship the outline with ‘confirm’ flags, then close the top three gaps. That rhythm turns messy interviews into reliable, repeatable case studies.

[aaron]

Strong upgrade — your Delta Detector + Evidence Ledger combo turns noise into numbers. Let’s stack two more accelerators so you can ship a CFO‑ready outline with zero guesswork and fast approvals.

Try this now (under 5 minutes)

• Paste your current results bullets into the Opener Sprint prompt below. You’ll get three punchy openings (CFO, operator, peer) that lead with a verified metric and timeframe. Pick one and lock it as your headline.

Copy‑paste prompt — Opener Sprint

“Using the verified items in my Evidence Ledger and outline bullets below, write three alternative opening sentences: (a) CFO/results‑first, (b) operator/story‑first, (c) peer/teach‑and‑apply. Rules: include one priority metric with its delta and timeframe; max 22 words; no adjectives like ‘significant’; cite the source tag [e.g., L41–47]. Inputs: Outline bullets [PASTE]; Evidence items [PASTE]; Priority metric [TEXT].”

Why this matters: Executives fund what they can measure. Openers and outlines that front‑load verified deltas, timeframes, and sources get green‑lit faster and repurposed across sales assets without rework.

Lesson from the trenches: Most case studies stall because claims don’t ladder up to a business outcome or quotes lack authority. Solve both with a Results Ladder and a Quote Authority pass before you assemble the final outline.

What you’ll need

• Your Evidence Ledger (claims → source → status)
• Delta Detector output and any Normalizer fixes
• Two minutes to score quotes by credibility

Step‑by‑step (fast, defensible, audience‑ready)

1. Build a Results Ladder (5–7 min): Make every metric roll up to a business outcome (e.g., revenue, cost, risk). Use the prompt below.
2. Tag quote authority (2–3 min): Keep quotes from roles closest to the metric owner (e.g., Ops lead for cycle time). Replace weak lines before design.
3. Compose the outline: Run your Outline Composer (from your last step) but feed it the Ladder and top‑scored quotes only. Ask for a results‑first opener for CFOs and a story‑first variant for operators.
4. Create a one‑page Slide Map (3–5 min): Turn the outline into a 6‑slide blueprint so sales can deploy it immediately.

Copy‑paste prompt — Results Ladder

“Create a Results Ladder from the items below. For each result, show: Level 1 Business Outcome (revenue/cost/risk/experience), Level 2 Operational Metric, Level 3 Leading Indicator, SOURCE tag, STATUS (verified/estimate/confirm), and two Missing‑Link questions if any level is missing. Prioritize CFO‑relevant outcomes. Inputs: Evidence Ledger [PASTE]; Results bullets [PASTE].”

Copy‑paste prompt — Quote Authority Scorer

“Score each quote on Credibility (1–5: role seniority + proximity to metric) and Specificity (1–5: numbers, clear verbs). Return the top 3 quotes only, with SPEAKER, LOCATION, and WHY IT MATTERS (one line). If all scores <7 combined, propose a crisper verbatim alternative using the nearest context (do not invent). Inputs: Quotes [PASTE].”

Copy‑paste prompt — 6‑Slide Map

“Map this case study to six slides. For each slide, return: TITLE, 3 BULLETS, METRIC CALLOUT (delta + timeframe + source tag), and QUOTE SUGGESTION (speaker + location). Slides: (1) Problem & impact, (2) Baseline, (3) Approach, (4) Results (numbers first), (5) Evidence & definitions, (6) Next steps/CTA. Use only verified or marked [confirm] items. Inputs: Final outline [PASTE]; Results Ladder [PASTE].”

What to expect

• A one‑page outline that leads with a verified metric, timeframe, and source tag
• A Results Ladder linking operational wins to business outcomes
• 2–3 high‑authority quotes with locations for fast approval
• A 6‑slide blueprint your sales team can deploy immediately

Metrics to track (own the outcomes)

• Time to outline: start → final outline (target: <25 minutes)
• Verification ratio: verified metrics ÷ total metrics (target: ≥80%)
• Evidence coverage: claims with source tags ÷ total claims (target: 100%)
• Quote authority score: average Credibility+Specificity (target: ≥7/10)
• Sign‑off speed: outline → executive approval (target: ≤3 business days)

Mistakes & fixes

• Orphan metrics (no outcome) → Run the Results Ladder; if no Level 1, demote or drop the claim.
• Soft quotes → Use the Authority Scorer; replace with a line from the metric owner or add a number.
• Mixed periods → Re‑run the Normalizer; split results by timeframe.
• Vague CTAs → Ask the 6‑Slide Map to propose a precise next step tied to the primary metric.

One‑week rollout

• Day 1: Run Delta Detector on two interviews; start the Evidence Ledger.
• Day 2: Normalize units/timeframes; build the Results Ladder.
• Day 3: Score quotes; capture top 2–3; request any missing verbatim lines.
• Day 4: Compose two outlines (CFO/story). Generate three openers via Opener Sprint.
• Day 5: Build the 6‑Slide Map; draft executive summary and CTA.
• Day 6: Verify remaining ‘confirm’ items; tighten to one metric per section.
• Day 7: Review KPIs (time, verification ratio, sign‑off speed). Lock your template.

Insider trick: Force one “money metric” to the top. Ask: “If a CFO could only see one number from this study, which is it and why?” Then open with that number, its delta, and timeframe — everything else supports it.

Your move.

[Author]

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