Jeff Bullas

Tell me your exam and target score, and I’ll sharpen this for you. Meanwhile, here are the fastest levers by exam, a ready-to-run AI prompt, and a simple routine that turns recordings into visible gains within a week.

What you’ll need

• Quiet spot, phone or laptop recorder (30–90 second clips).
• Your exam rubric or the 3 criteria you care about (for example: fluency, pronunciation, coherence).
• An AI tool that accepts audio or transcripts, plus a place to log results (notebook or one spreadsheet column).

High-impact focus by exam (pick 2 levers)

• IELTS Speaking:
  • Fluency/Coherence: reduce pauses; use fixed linking set (for example: first, on the other hand, to wrap up).
  • Pronunciation: thought groups and word stress (stress the key word in each group).
• TOEFL Speaking:
  • Delivery: steady pace (130–160 wpm) and clear stress.
  • Topic Development: 15-second plan → main idea + two supports → short wrap.
• Cambridge B2/C1:
  • Interactive Communication: turn-taking phrases and asking for clarification.
  • Range/Accuracy: upgrade sentences with relative clauses and contrast linkers.
• DELF/DALF (French):
  • Coherence of argument: signpost your stance and two reasons.
  • Pronunciation: rhythm and liaison in common phrases.
• OPIc:
  • Narration in past/description in present: time markers + who/what/where.
  • Elaboration: add sensory detail or a “because” clause to each point.

Insider trick: Limit yourself to a 10-phrase linking toolkit for two weeks. Reusing the same skeleton raises coherence fast without extra mental load. Pair it with thought-group reading: speak in 4–7 word chunks with a micro-pause, stressing the key word in each chunk.

Run a focused AI session (10–15 minutes)

1. Pick one prompt. Record 60–90 seconds.
2. Send the audio or transcript to the AI with the prompt below. Ask it to assess only your 2 chosen levers.
3. Do the 3 micro-drills it returns (each 1–3 minutes). Re-record the same prompt.
4. Request a progress check that looks only at the earlier weaknesses. Log your KPIs.

Copy-paste AI prompt (general)

You are an experienced examiner-coach for [EXAM NAME] at [TARGET LEVEL/SCORE]. Assess the attached [TRANSCRIPT or AUDIO LINK]. Focus on these two criteria: [CRITERION 1], [CRITERION 2]. Output exactly:
1) One-sentence overall verdict; 2) A tiny scorecard (0–10) for each chosen criterion; 3) Two strengths; 4) Three precise weaknesses with timestamps or exact phrases; 5) Three micro-drills (30–120s each) with step-by-step instructions and success criteria; 6) A 60–90 second re-record challenge prompt that forces me to apply today’s fixes; 7) A KPI row I can log: words per minute, average pause length (s), pronunciation slips per 60s, structure used (yes/no).

Variants (use when you want a narrower focus)

• Pronunciation-only: Assess only thought groups, word stress, and any recurring sound errors. Provide minimal-pair lists and 2 shadowing sentences at slow/normal pace with stress marks.
• Fluency-only: Give me a timed script with 3 planned pauses and target pace 140 wpm. Flag filler words and provide a replacement pause strategy.
• Coherence-only: Score my opening, two points, and wrap (0/1 each). Suggest a fixed 4-line answer skeleton for me to reuse.

Example: 5-minute micro-drills

• Thought-group shadowing (2 minutes): Listen to a model sentence split into 5–7 word chunks; repeat twice, accenting the bolded word each chunk.
• Filler swap (1 minute): Replace “uh/um/like” with a silent 0.3s pause. Read a short paragraph and mark each planned pause.
• Linking ladder (2 minutes): Speak one idea using the same 4-link sequence: “To begin… For example… However… To sum up…”

What to expect

• Within 3–4 sessions: shorter average pauses, clearer stress, and a predictable answer shape.
• Track three KPIs: words per minute, average pause length, and yes/no for “opening + two points + wrap.” Aim for 10–15% improvement on your weakest KPI in week one.

Common mistakes and quick fixes

• Vague feedback: Always demand timestamps or exact phrases. No specifics, no progress.
• Overlong recordings: Keep it to 60–90 seconds so the AI can zoom in.
• Fixing too much: Two levers per week beats five half-fixed problems.
• No baseline: Log Day 1 metrics and re-record the same prompt on Day 4 to see clear change.

7-day plan (15 minutes per day)

1. Day 1: Choose exam and two levers from the list above. Record one prompt, run the general AI prompt, log KPIs.
2. Day 2–3: Do the 3 micro-drills daily. One new 60–90 second recording each day, quick AI check on the same two levers.
3. Day 4: Re-record Day 1 prompt. Ask for a progress-only review versus Day 1 weaknesses. Update KPIs.
4. Day 5: Swap in a second prompt. Keep the same two levers.
5. Day 6: Narrow to the single biggest blocker (for example, misplaced stress). Do 2x the reps on that drill.
6. Day 7: Mini-mock: 3 prompts back-to-back. Get a concise rubric score and compare KPI trend to Day 1.

Your turn

• Reply with: exam name, target score/level, exam date, and your two biggest worries (for example: “long pauses” and “uncertain structure”).
• If you prefer, paste a short transcript (60–120 words) and I’ll return a tailored drill set for your exam’s top two levers.

Keep it simple: the same short skeleton, the same two levers, and daily reps. That’s how you turn practice into points.

Spot on: “documentation beats doubt.” Let’s turn that into a simple, repeatable system so you move fast, learn more, and stay well inside integrity rules.

Do / Do not (quick checklist)

• Do keep a changelog of AI help, your edits, and the sources you verified.
• Do rewrite in your own voice and add proper citations you checked yourself.
• Do include a brief AI-use note (what tool, what for, what you verified).
• Do map each paragraph to your rubric so you only include what’s assessed.
• Do not submit AI text verbatim or accept AI-suggested citations without checking.
• Do not hide AI use on graded tasks where your policy requires disclosure.
• Do not ask AI to “do my assignment” — ask it to coach you.

What you’ll set up once (5–10 minutes)

• Integrity Pack (3 small files):
  • Changelog.txt — records the AI prompt, its output summary, and your edits.
  • Source-Check Table.doc — a simple table: Claim | Source title/author/year | How I verified | Quote/idea | In-text cite.
  • Disclosure Note.doc — one sentence explaining tool and purpose.

15-minute “Integrity Cycle” (repeat for each paragraph)

1. Aim (1 min): Write your learning goal and rubric criteria for this paragraph.
2. Coach, not ghost (4–6 min): Use the prompt below to get suggestions, not a finished paragraph. Ask for tags like [CHECK FACT] and [CITE] so you know what to verify.
3. Verify (5–7 min): For each [CITE] item, find the source, read the abstract or section, confirm details, and complete your Source-Check Table.
4. Rewrite (3–5 min): Put it in your voice. Read aloud. Add proper in-text citations.
5. Log & disclose (1 min): Add one or two lines to your Changelog.txt and keep the disclosure note with your submission files.

Copy-paste AI prompt (guardrails included)

Please act as a writing coach for an undergraduate assignment on [TOPIC]. Do not write the full paragraph. Instead:
1) Suggest a clear outline for one paragraph that aligns with these rubric points: [PASTE RUBRIC POINTS].
2) List the specific claims that require sources and label each with [CITE].
3) Mark any statements that are uncertain or debatable with [CHECK FACT] and explain why.
4) Suggest search keywords and 2 types of reputable sources to look for (e.g., peer-reviewed article, government report). Do not fabricate citations or DOIs.
5) Provide 3 sentence-level rewrites of my draft that keep my meaning but simplify language; mark any words you reused verbatim from me with [VERBATIM].
Here is my draft paragraph: “[PASTE YOUR PARAGRAPH]”

Insider upgrade: after you verify sources, ask the AI to insert only APA/MLA placeholders where citations belong (e.g., (Author, Year)), and you fill in full references from the real source. This keeps accuracy under your control.

Worked example (from messy to compliant)

• Original student sentence: “Social media harms teen mental health a lot.”
• AI coaching output (summary):
  • Outline: define “harms,” cite longitudinal data [CITE], note nuance (usage type, duration).
  • Claims to verify: links between heavy use and depressive symptoms [CITE]; differences by age/gender [CHECK FACT].
  • Search terms: “adolescent social media depressive symptoms longitudinal,” “screen time mental health meta-analysis.”
  • Rewrites (samples): “Several studies associate heavy social media use with depressive symptoms in adolescents [CITE]. The effect varies by how platforms are used [CHECK FACT].”
• Student verification:
  • Find one meta-analysis and one government health report; read abstracts; confirm measures and limitations.
  • Fill Source-Check Table with titles/authors/years and short notes.
• Final student rewrite (own voice): “Evidence from recent reviews links heavier social media use with higher depressive symptoms among adolescents, though effects differ by how teens engage online (Author, Year; Author, Year).”
• Changelog note: “AI suggested outline and flagged [CITE]/[CHECK FACT]. I located two sources, confirmed measures, and rewrote in my voice. Added APA in-text placeholders and full references.”

Mistakes to avoid and quick fixes

• Copying AI text: If any sentence is still close to the AI’s wording, paraphrase again and read aloud. Aim for your natural rhythm.
• Phantom citations: Never paste a reference you did not open. If unsure, delete it and replace with a verified source.
• Over-claiming: If your source shows correlation, avoid causal language. Ask the AI to flag causal verbs for you to tone down.
• Policy blind spots: If the task bans AI, don’t use it. If unclear, ask your instructor how to disclose permissible editing help.

Templates you can copy into your files

• Changelog.txt: Date | Task | AI prompt (short) | What AI gave | What I changed | Why I changed it.
• Source-Check Table: Claim | Source (Title, Author, Year) | How verified (abstract/section) | Quote/idea | In-text cite.
• Disclosure Note: “I used an AI assistant for language refinement, outlining, and identifying where citations were needed. I verified sources, rewrote content in my own words, and take responsibility for the final work.”

What to expect (realistic)

• Drafting feels faster and clearer because you know exactly what to verify.
• You’ll trade raw speed for confidence: 20–40% time saved on brainstorming, 10–30 minutes added per paragraph for verification and rewriting.
• Your files form an audit trail that shows learning, not shortcutting.

One-week action plan (light lift)

1. Day 1: Create your Integrity Pack files.
2. Day 2: Run the prompt on one paragraph; add [CITE]/[CHECK FACT] tags.
3. Day 3: Verify two sources and complete the Source-Check Table.
4. Day 4: Rewrite in your voice; add in-text citations and references.
5. Day 5: Add disclosure; store AI chat transcript with your draft.
6. Day 6–7: Repeat for the next paragraph; do a final read-aloud pass.

Keep it simple: coach, verify, rewrite, document. That’s how you use AI to learn faster, protect your integrity, and submit with confidence.

Hook: Love the micro-routine — now let’s use AI to cut the fiddly prep so you can flip more lessons without burning evenings.

Quick context

If you already do a 2–3 minute video + 2-question check, AI can write the script, make captions, generate a tight diagnostic and give you three ready-to-run in-class activities. That turns one lesson a week into a sustainable habit.

What you’ll need

• A phone or tablet to record
• Your LMS or a shared folder to post videos
• A simple quiz tool (Google Forms, LMS quiz, paper alternative)
• An AI chat assistant (any online AI that accepts text prompts)

Step-by-step (do this in 10–15 minutes)

1. Write one clear objective: one sentence that students should be able to say back.
2. Copy the AI prompt below and paste your objective in the bracket. Run it once for script + checks, run again if you want a second style.
3. Record a 90–120s video using the AI script. Keep it conversational and show one worked example.
4. Paste the AI text into your caption tool or your LMS captions field (quick copy-paste).
5. Create the 2-question check (MCQ auto-scored + one short answer). Assign it before class.
6. Before class, group students by results (ready / needs help / extension). Use the AI activities for group work and finish with a 1-question exit ticket.

Copy-paste AI prompt (main)

“Create a 90–120 word teacher script explaining the objective: [INSERT OBJECTIVE]. Include one clear worked example and one quick question for students. Then provide: (A) a 3-option multiple-choice question with the correct answer and a one-sentence explanation, (B) a one-sentence short-answer question to check understanding, and (C) three 10-minute in-class activities: one for students who are ready, one for students who need help, and one for students who need extension. Also provide a 15-word caption transcript for video captions and a 1-question exit ticket tied to the objective.”

Prompt variants

• Low-tech: Add “Format the MCQ and short answer so I can print them on a paper slip.”
• Older students: Add “Use adult-friendly language and one real-world example relevant to finance/work.”

Example (filled prompt)

Objective: “Explain how to calculate simple interest for one-year loans.”

AI output you’ll get: a 100-word script with a worked example (I borrow $500 at 6% → interest $30), a 3-option MCQ (correct answer + brief why), a one-sentence short-answer (show the formula and a number), three 10-minute activities (peer-teach, guided practice with hints, and an extension problem), a 15-word caption and a one-question exit ticket.

Common mistakes & fixes

• Mistake: Prompt too vague. Fix: Add the exact objective and audience level in the prompt.
• Mistake: Overlong video. Fix: Trim script to 90–120 words and rehearse once.
• Mistake: No follow-up plan. Fix: Use the three activities exactly as the group plan for class.

1-week action plan

1. Day 1: Pick one lesson and write the single objective.
2. Day 2: Run the prompt, record the video, add captions.
3. Day 3: Create and assign the 2-question check.
4. Day 4: Group students and run the flipped lesson.
5. Day 5: Review the exit ticket and note one improvement for next week.

Closing reminder: Start small, reuse scripts each term, and measure one metric (pre-class completion). Five minutes a week invested now will save you hours later.

You’re 90% there. To make your interactive case studies reliable (not just clever), lock the scoring with anchors, auto-generate analytics, and ship a sales-ready summary. That’s how you get repeatable results without extra headcount.

What you’ll need

• One KPI with a baseline (e.g., current onboarding time = 21 days).
• Three “anchor” outcomes you already trust (best, typical, worst) with real numbers.
• LLM access and a simple surface (web page, modal, chat widget).
• Analytics that can log named events + a lightweight CRM handoff.
• Two reviewers: one subject-matter, one customer-facing (sales or CS).

Build it in six moves

1. Set your scoring rails. Define ranges and a simple formula. Example: Impact% range −10 to +40. Fit 0–10. Readiness 0–5. LeadScore = 0.6*Impact(normalized to 0–10) + 0.3*Fit + 0.1*Readiness. Set a threshold (e.g., ≥7.0 = qualified).
2. Create three anchors. Write short, numeric anchor cases the AI must align to: Worst (−5% impact), Typical (+12%), Best (+35%). These calibrate the model and stop hand-wavy numbers.
3. Draft a tight 5-step flow. Context → Decision 1 → Decision 2 → Outcome → Debrief. Three choices per decision. Keep copy to 40–60 words per screen.
4. Name your analytics events. Use a pattern you can sort: scenario_slug.decision1.choiceA, scenario_slug.complete, scenario_slug.lead.captured. Consistent names make dashboards trivial.
5. Design the debrief as a mini ROI card. Show chosen path, Impact%, LeadScore, and two next steps (e.g., book ROI audit, start 14‑day pilot). Gate contact capture only if LeadScore ≥ threshold.
6. Run a calibration pass. Ask the AI to check its outputs against anchors, flag drift, and adjust. This keeps your numbers believable.

Copy‑paste prompt (anchored, sales‑ready)

Act as an interactive case study builder for senior managers. Goal: generate a 5‑step scenario with scored choices that align to the anchors below and produce a sales‑ready summary.

Inputs you’ll get from me next: (A) short context (100 words), (B) primary KPI and baseline value, (C) three decision points, (D) three numeric anchors: Worst, Typical, Best with impact% and a one‑line reason.

Do this:

• For each of the three decision points, produce 3 concise choices. For each choice include: (1) one‑sentence immediate consequence, (2) estimated Impact% on the KPI, (3) Fit 0–10, (4) Readiness 0–5, (5) a one‑line rationale.
• Normalize Impact% to 0–10 for LeadScore = 0.6*Impact(norm 0–10) + 0.3*Fit + 0.1*Readiness. Show the numeric LeadScore for each choice.
• Calibration: align Impact% to the anchors. If any choice is outside the implied range, adjust and note “(anchored)”. Add a Confidence 0–100% for each choice.
• After Decisions 1–3, synthesize the most likely chosen path (based on highest average LeadScore), then produce the Outcome and a Debrief (2 short paragraphs: what went well, what to fix next).
• Generate analytics event names for each choice using the pattern [slug.decision#.choice#].
• Finish with a single‑line CRM summary: “Path: … | Impact%: … | LeadScore: … | Next steps: … | Persona fit: …”. Keep language plain English.

Constraints: 40–60 words per screen, no jargon, numbers must be inside the anchor range unless flagged as an exception and justified.

Two fast variants

• Training variant: After the debrief, add two quiz questions with model answers and a short tip to improve the user’s last choice.
• Qualification variant: Ask two follow‑ups to confirm budget and timeline; if both are positive and LeadScore ≥ threshold, propose a specific next step (demo, pilot, ROI workshop).

Example (short)

• Context: Mid‑market SaaS wants to cut onboarding time (baseline 21 days).
• Anchors: Worst −5% (no change management), Typical +12% (playbook + email nudges), Best +35% (guided setup + in‑app walkthroughs).

• Decision 1: Onboarding approach
  • A) PDF checklist. Consequence: slow adoption. Impact +3% (anchored), Fit 6, Readiness 5, LeadScore 5.1
  • B) Email nudge series. Consequence: moderate acceleration. Impact +10% (anchored), Fit 7, Readiness 4, LeadScore 6.4
  • C) In‑app walkthroughs. Consequence: faster time‑to‑first‑value. Impact +28% (anchored), Fit 8, Readiness 3, LeadScore 7.6
• Decision 2: Data migration
  • A) Manual import. Impact +2%, Fit 5, Readiness 5, LeadScore 4.9
  • B) CSV templates. Impact +9%, Fit 7, Readiness 4, LeadScore 6.2
  • C) Assisted import. Impact +18%, Fit 8, Readiness 3, LeadScore 7.0
• Decision 3: Change management
  • A) None. Impact −3%, Fit 4, Readiness 5, LeadScore 3.7
  • B) Champions + office hours. Impact +12%, Fit 8, Readiness 4, LeadScore 7.1
  • C) Exec kickoff + incentives. Impact +20%, Fit 9, Readiness 3, LeadScore 7.4

Likely path: C → C → C. Outcome: Estimated Impact +30% (anchored within Typical–Best). Debrief: Highlight guided setup + assisted import + exec sponsorship; next steps: pilot 10 users, measure days‑to‑activation; roll out org‑wide if improvement ≥25%.

Common mistakes and quick fixes

• Anchor drift: Numbers creep larger over time. Fix: restate anchors in every prompt and force a Confidence score; review low‑confidence items weekly.
• Vague debriefs: “It depends” kills momentum. Fix: require two specific next steps with owners and timelines.
• Wall‑of‑text screens: People bail. Fix: 50‑word limit per screen; prefer verbs and outcomes.
• No control path: You can’t prove lift. Fix: include a “do nothing” or minimal path to benchmark gains.

Action plan (5 days)

1. Day 1: Pick one KPI and write three anchors. Define event names and the LeadScore threshold.
2. Day 2: Use the anchored prompt to draft choices and debrief. Add Confidence and adjust any out‑of‑range numbers.
3. Day 3: Build the scenario in your no‑code surface. Instrument events. Add the CRM summary to your form handoff.
4. Day 4: Test with two reviewers. Cut copy by 20%. Fix any unclear choices. Validate scores against anchors.
5. Day 5: Soft launch. Watch engagement, completion, and qualified conversion. Iterate the lowest‑performing screen first.

Closing thought

Ship one anchored, scored scenario. Measure five signals. If it doubles time‑on‑page and yields even a handful of qualified leads, clone the pattern. Consistency beats complexity — and anchors make your numbers trustworthy.

Quick win (try in 3 minutes): paste one paragraph of your essay into an AI tool and use the disclosure line below on your draft. You’ll see how easy it is to keep AI as an assistant — not the author.

Good point from above: treating AI as a drafting assistant and documenting its use is the right mindset. Here’s a practical, easy-to-follow system you can use today to avoid academic misconduct and actually learn more.

What you’ll need:

• Your draft or paragraph to improve
• Access to an AI chat tool (free or paid)
• Your course rubric and citation guide (APA/MLA)
• A simple changelog (Google Doc or plain text file)
• 5–30 minutes per revision session

Step-by-step: a simple workflow:

1. Save the original paragraph and copy it into your changelog as “Original.”
2. Use the prompt below in your AI tool to get a suggested rewrite and source suggestions.
3. Verify each source the AI suggests — search the title/author, open abstracts or PDFs, and confirm facts (5–10 minutes per source).
4. Rewrite the AI output into your own words and rhythm. Read it out loud — if it sounds like you, it’s good.
5. Add proper in-text citations and a reference list entry from the verified source.
6. Log what the AI provided and what you changed (1–2 lines per paragraph). Add a disclosure line to your cover sheet.

Copy-paste AI prompt (use this exactly):

Please improve the following undergraduate essay paragraph on [TOPIC]. Keep the original meaning, simplify language to a clear undergraduate voice, mark any sentences you retain verbatim with [VERBATIM], suggest two reputable sources (title, author, year) with short notes on why each is relevant, and add APA-style in-text citation placeholders. Then give a one-paragraph summary explaining what you changed and why. Here is the paragraph: “[PASTE YOUR PARAGRAPH]”

Example disclosure lines you can copy:

• Cover sheet: “This submission used an AI drafting assistant to refine language and suggest sources. All AI contributions were verified and edited by the author.”
• Method section: “AI tool used: [tool name]. Purpose: editing and source suggestions. I verified and rewrote all content and added citations.”

Common mistakes & fixes:

• Submitting verbatim AI text — Fix: rewrite until it matches your voice and log the change.
• Believing AI citations are real — Fix: confirm titles/authors in Google Scholar or your library database.
• Not disclosing AI use — Fix: add one of the simple disclosure lines above.

2-step mini action plan (this week):

1. Day 1: Read your school’s AI policy and save it with your notes.
2. Day 2: Revise one paragraph using the prompt above; verify sources and add disclosure.

What to expect: faster drafting, a small time cost to verify and rewrite, and a clear record that protects your academic record while improving learning. Try the quick win now — it takes minutes, and you’ll build the habit that prevents big problems later.

Quick win: Try this now — paste one sentence from your marketing email into your AI and ask: “Simplify to Grade 8 reading, use active voice, remove idioms.” You’ll get a clearer sentence in under 30 seconds.

Good call on the checklist you shared — clear instructions + one KPI = faster, measurable edits. Here’s a compact, practical add-on you can use immediately to turn that workflow into repeatable wins.

What you’ll need

• Original copy (headline or paragraph)
• One-line audience note (age, country, role)
• Desired tone (formal / conversational)
• Main KPI (open rate, CTR, conversion)

Step-by-step (do this in 10–15 minutes)

1. Paste the original copy into the AI. Ask for a single-sentence simplification first — this exposes big clarity issues fast.
2. Then run two variant requests (formal and conversational). Ask for: 6-word subject, 120–150 word body, one-line CTA, and a 3-bullet change summary.
3. Run a cultural/local check: “Remove references that assume US culture and suggest neutral alternatives for [country].”
4. Pick two best variants and schedule an A/B test (subject + body). Run for 48–72 hours, measure CTR and conversion lift.

Robust copy-paste AI prompt (use as-is)

Rewrite the following marketing email for clarity and conversion. Audience: business owners, 35–60, non-native English speakers in [country]. Goal: increase CTR. Constraints: 120–140 words, simple sentences, active voice, no idioms, one clear CTA. Provide two variants: Version A (formal) and Version B (conversational). For each: give a 6-word subject line, the body, a 12–15 word CTA, and a 3-bullet summary of the main edits.

Worked example (before → after)

• Before: “We would be delighted to have you avail our new service that could potentially boost your revenue.”
• After (conversational): “Try our new service to grow your revenue this quarter. Start a free trial.”

Common mistakes & fixes

• Mistake: Vague instructions. Fix: Specify audience, tone, length, and KPI.
• Mistake: One-off edits without templates. Fix: Save winners as reusable templates.
• Mistake: Skipping cultural check. Fix: Always run a localization pass for the target market.

7-day mini action plan

1. Day 1: Pick 3 priority emails and set KPIs.
2. Day 2: Run AI rewrites with the prompt above.
3. Day 3: Localize and choose top 2 per email.
4. Day 4: A/B test subjects and bodies.
5. Day 5–7: Monitor, pause losers, keep the winner as a template.

Reminder: Treat AI as your polishing station — give it clear goals, test results, and repeat what works.

Nice concise workflow — the CHANGELOG and short AI summaries are the real time-savers. I’ll add a few practical layers that make this approach even more reliable for non-technical teams.

Why this helps: small rules + a simple AI prompt remove confusion, reduce overlaps and save the back-and-forth emails that waste time.

What you’ll need:

• A shared cloud folder (one place everyone uses).
• A CHANGELOG.txt file in the root.
• A Drafts folder and a Final folder.
• A simple status token system (a tiny file or document label: Editing, In Review, Locked).
• An AI tool you can paste text into (chat or assistant).

1. Start — Copy the master into Drafts and rename: Project_YYYYMMDD_Initials. Add a one-line entry to CHANGELOG with file name and status “Editing”.
2. Edit — Work on your copy. When finished, paste the changed section or the two versions into the AI (see prompt below) and ask for a 2–3 bullet summary and a one-line changelog entry.
3. Publish summary — Paste the AI bullets into CHANGELOG next to the file name and timestamp. Change status to “In Review.”
4. Review — Reviewer adds comments in the document and updates CHANGELOG with their review note. If accepted, move file to Final and update the master name (e.g., Project_v2_Master).
5. Resolve conflicts — If two edits clash, ask the AI to list the differences and suggested merged wording; the reviewer picks the final version and notes the decision in CHANGELOG.

Copy-paste AI prompt (use as-is):

Compare these two versions of a paragraph. Above the line is the ORIGINAL, below the line is the EDITED. Show 3 bullets: (1) What changed, (2) Why this matters (impact), (3) Any unresolved questions or decisions. Then give a one-line CHANGELOG entry in this format: Filename | YYYY-MM-DD | Initials: summary.

Example:

1. File: Report_20251122_JS — AI summary: “Clarified the executive summary to focus on customer outcomes; tightened language; removed outdated metric table. Open question: should we add updated Q4 numbers?” — Add that line to CHANGELOG.
2. Move to Final only after reviewer confirms Q4 decision and updates master.

Common mistakes & fixes:

• Mistake: Multiple people edit same draft. Fix: Enforce status tokens or quick chat note before editing.
• Mistake: Vague changelog entries. Fix: Require the AI one-line summary format so entries are consistent.
• Mistake: Changelog gets ignored. Fix: Schedule a 5–10 minute weekly tidy and enforce max 3 active drafts.

Quick 5-step action plan to implement today:

1. Create Drafts, Final and CHANGELOG.txt in your shared folder.
2. Agree on the filename pattern and status tokens with the team (10 minutes).
3. Use the AI prompt above for the next edit and paste the result into CHANGELOG.
4. Limit to 3 active drafts and set a weekly 10-minute tidy-up meeting.
5. After two weeks, review the process and tighten rules where needed.

Small habits win. Start with the changelog + one AI summary per edit — you’ll see fewer conflicts and faster approvals.

— Jeff

Your week-one plan nails the balance: speed from MT, quality from a tight 20–30 minute native edit, and learning from A/B tests. Let’s add a lightweight control layer so you ship with confidence and cut reviewer time further.

Try this in 5 minutes (quick win)

Before translating, run a preflight “locale risk scan” so you know what to adapt beyond words.

Copy‑paste prompt: Read the English marketing copy below. For [LANGUAGE] in [COUNTRY], list the top 10 localization risks and fixes. Cover: tone (formal/informal), idioms to replace, number/date/currency formats, units (sizes, decimals), legal/claim phrasing to soften, character limits by placement (subject line, ad headline), culturally sensitive words, CTA phrasing options (2), and any image/icon pitfalls. Output a checklist I can hand to a reviewer, plus a one‑sentence local positioning tweak. Text: [PASTE COPY]

Why this helps: You hand your reviewer a focused checklist, not a blank page. Expect a clearer brief and fewer rewrites.

What you’ll need (simple “localization ops board”)

• One spreadsheet with columns: Asset, Market, Language, Tone (3 adjectives), Formality (e.g., tú/usted; du/Sie), Currency/Date format, Character limits (subject line/ad), 5‑term glossary, Forbidden words, Legal notes, Reviewer, Status, Notes.
• A micro‑glossary per language (3–7 must‑keep terms + 3 banned terms).
• A two‑line reviewer checklist (tone/glossary, legal/CTA).

Step‑by‑step (stack this on your current flow)

1. Preflight (10–15 min per market): Run the risk scan prompt above. Fill your ops board columns for each asset.
2. Draft (5 min): MT your asset. Keep your glossary on screen to correct brand terms immediately.
3. AI “linter” pass (5–10 min): Use the QA prompt below to auto‑flag tone slips, glossary misses, units, numbers, and legal phrasing before a human sees it.
4. Native post‑edit (20–30 min): Give the editor the MT draft, the checklist, and ask for two CTA options plus one cultural note.
5. QA & deploy (15–30 min): Spot‑check links, numbers, dates, currency symbols, and character limits. Launch your small test (one ad set + two subject lines).
6. Measure (7–14 days): Track CTR, opens, conversion, and support contacts per locale. Update the glossary weekly with what worked.

Insider trick: freeze 3 non‑negotiables

• Brand terms: names and product descriptors never change (kept in glossary).
• Legal claims: exact phrasing or safer local variant (pre‑approved).
• CTA formula: structure stays (e.g., Verb + Benefit), words flex per market.

Robust, copy‑paste AI prompts (use as written)

1) Locale‑fit preflight (before MT) I’m localizing for [LANGUAGE] in [COUNTRY]. Review the English copy below and produce: a) tone recommendation (formal/informal + 3 adjectives), b) 5 risky idioms with safer rewrites, c) number/date/currency rules, d) units to convert, e) 2 legal/claim risks with safer wording, f) character limits by placement to watch (subject line/ad/CTA), g) 2 local CTA options. Output as a checklist for the editor. Copy: [PASTE]

2) AI linter QA (after MT, before human) You are a localization QA checker for [LANGUAGE] in [COUNTRY]. Check the localized text against this brief and glossary. Flag: tone mismatch, glossary violations, idioms that don’t land, number/date/currency errors, unit issues, character limit overages, and legal risk phrases. Output: issue → severity (High/Med/Low) → suggested fix. Brief: [PASTE]. Glossary: [TERM = Approved translation]. Localized text: [PASTE]

3) Transcreation booster (for CTAs/subject lines) Rewrite the CTA and subject line for [LANGUAGE] in [COUNTRY]. Keep the core promise: [VALUE PROP]. Provide 3 options each: one direct, one benefit‑led, one urgency‑soft. Max: subject line 45–50 characters, CTA 2–3 words. Avoid [FORBIDDEN WORDS].

Example (how it plays out)

• Markets: Germany (DE), Mexico (MX).
• Tone: DE = formal “Sie”, confident, precise; MX = friendly “tú”, warm, clear.
• Formats: DE = € 29,90 and 24‑hour time (15:30); MX = $ 299.00 MXN and DD/MM/AAAA dates.
• CTA variants: DE: “Jetzt entdecken”, “Vorteile sichern”. MX: “Empieza hoy”, “Conócelo ahora”.
• Legal nudge: “Dermatologically tested” → DE: “Dermatologisch geprüft” (avoid implied outcomes). MX: “Probado dermatológicamente”.

10‑minute LQA checklist (give to editors)

• Tone matches brief (formal/informal) and 3 adjectives are evident.
• All glossary terms used exactly; banned terms absent.
• Numbers, dates, currency and units localized (commas/points, symbols, sizes).
• CTAs and subject lines fit character limits and sound native.
• Claims softened per legal note; no absolute promises.
• Visuals fit culture (no flags/hand signs that misfire).

Mistakes & fixes (beyond the obvious)

• Over‑formal or over‑casual tone. Fix: lock formality in the brief and include a sample sentence.
• Decimal/spacing errors (e.g., €29.90 vs € 29,90). Fix: add format examples in the ops board; linter checks numbers.
• AM/PM in 24‑hour markets. Fix: mandate 24‑hour time in formats column.
• Spain Spanish used in Mexico (or vice versa). Fix: set locale explicitly in every prompt and in the ops board.
• Truncation on ads/emails. Fix: set character limits and test two length variants.

Action plan (plug into your current week)

1. Today (1 hour): Create the ops board, run the preflight prompt for two markets, write a 5‑term glossary and forbidden words list.
2. Same day: MT the pilot asset, run the linter prompt, then send to the reviewer with the two‑line checklist and a 30‑minute cap.
3. Tomorrow: Quick QA, launch one ad set + two subject lines per market. Start tracking metrics and note two issues per locale.
4. Next week: Update glossary with learnings, roll the process to the next two assets.

Pragmatic, repeatable, and fast. Stack these prompts and the ops board on your existing plan and you’ll ship localized tests this week with fewer revisions and clearer KPIs.

Onwards — you’ve got this.