Your AI Action Plan

Create a research brief on [TOPIC] for [AUDIENCE]. Include: current landscape, 5 key facts, 3 risks, 3 open questions, and source links for every claim that affects a decision.

Review this research draft. Split it into: source-backed facts, reasonable inferences, unsupported claims, and questions to verify. Then rewrite the summary so unsupported claims are removed or clearly caveated.
Draft: [PASTE DRAFT]

Research [OPTIONS / VENDORS / TOPIC], compare them against [CRITERIA], and produce a recommendation memo. Include a table, tradeoffs, risks, source links, and the decision I should make if the priority is [COST / SPEED / QUALITY / RISK].

Create a research intake template for [ROLE / TEAM]. It should capture: decision to support, scope, time period, must-use sources, sources to avoid, comparison criteria, output format, approval owner, and caveats required before sharing.

Turn these rough process notes into a simple SOP. Include: purpose, when to use it, required inputs, step-by-step workflow, owner, tools, quality checks, and common mistakes.

Notes:
[PASTE]

Create a reusable checklist for [PROCESS]. Include sections for intake, execution, quality check, handoff, escalation, and completion evidence. Format it so I can paste it into Notion.

Convert this process explanation into: 1. SOP, 2. quick checklist, 3. training note for a new person, 4. edge cases, 5. questions to confirm with the process owner.

Transcript or notes:
[PASTE]

Design a procedure-library template for [TEAM]. Include fields for owner, last reviewed, inputs, steps, tools, examples, risks, escalation path, related templates, and update log. Add rules for when a procedure must be reviewed.

Write a [TYPE - cold outreach / follow-up / proposal / status update] email.
From: [YOUR ROLE] at [COMPANY]
To: [RECIPIENT ROLE] at [THEIR COMPANY]
Context: [1-2 sentences of background]
Goal: [what you want them to do]
Tone: [professional / friendly / direct]
Length: [short = 3 sentences / medium = 1 short paragraph / full = structured email]

Write a weekly marketing performance report.
Period: [DATE RANGE]
Metrics to include: [list your KPIs]
Highlights: [what went well]
Issues: [what underperformed and brief reason]
Next week priorities: [3 bullet points]
Audience: [manager / team / client]
Tone: factual, no fluff. Use bullet points for metrics, short paragraphs for narrative.

Turn these rough notes into a clear [EMAIL / STATUS UPDATE / REPORT].
Audience: [WHO WILL READ IT]
Purpose: [DECISION, UPDATE, REQUEST, OR ESCALATION]
Raw notes:
[PASTE NOTES]

Return:
1. Suggested subject line
2. Short summary
3. Main message in my tone: [DIRECT / WARM / EXECUTIVE]
4. Action items with owners and dates
5. Risks or open questions

I'll share 3 examples of emails I've written. After reading them, identify:
1. My typical sentence length and structure
2. Words or phrases I use often
3. My tone (formal / casual / direct / warm)
4. Things I never say

[PASTE EMAIL 1]
[PASTE EMAIL 2]
[PASTE EMAIL 3]

Now write a [TYPE] email using my style. Here's the context: [CONTEXT]

Clean this data before I enter it into [SYSTEM]. Return a table with columns: [COLUMNS]. Standardize dates, names, categories, and phone/email formatting. Add a final column called Review needed for anything uncertain. Do not invent missing values.

Raw data:
[PASTE]

Map this incoming form to my database fields. Required destination fields: [FIELDS]. Validation rules: [RULES]. Return a field mapping, transformations needed, and exception cases that should stop for human review.

Extract records from this input and prepare them for [SYSTEM]. Return: 1. clean records table, 2. duplicate warnings, 3. missing required fields, 4. values that need human review, 5. a short change log.

Input:
[PASTE]

Create a data-entry QA playbook for [PROCESS]. Include: required fields, allowed formats, duplicate checks, sensitive-data warnings, examples of good records, examples of records to reject, and a final human review checklist.

Summarize this meeting.
Notes or transcript: [PASTE]
Return: 1. short summary, 2. decisions made, 3. action items with owner and due date, 4. risks, 5. follow-up message draft.

Review this meeting transcript and create a follow-up note. Separate exact decisions from discussion points. Flag anything unclear. Transcript: [PASTE TRANSCRIPT].

Convert this meeting transcript into project tasks. For each task include owner, due date, dependency, priority, and a follow-up email paragraph. Transcript: [PASTE].

Create a meeting memory template for [TEAM / CLIENT]. For each meeting, capture decisions, action items, repeated themes, unresolved questions, stakeholder commitments, and items to revisit next time.

Translate this text from [SOURCE LANGUAGE] to [TARGET LANGUAGE].
Audience: [AUDIENCE]
Purpose: [PURPOSE]
Terminology rules: [PASTE]
Return: translation, uncertain phrases, cultural notes, terms to verify, and a post-edit checklist. Do not translate legal/medical/certified text without expert review.

Text:
[PASTE]

After document translation, create a QA checklist for this file.
Source summary: [PASTE]
Target language: [LANGUAGE]
Return: formatting checks, terminology checks, possible omissions, numbers/dates to verify, and client questions.

Review this machine translation against the source.
Source: [PASTE]
Draft translation: [PASTE]
Glossary: [PASTE]
Return: meaning errors, omissions, terminology mismatches, tone issues, formatting issues, and suggested fixes with explanations.

Create a translation intake and risk-routing system. Include: content types, confidentiality rules, legal/medical/certified flags, AI-allowed workflows, human review requirements, client disclosure notes, and final QA checklist.

Extract terminology candidates from this source text.
Text: [PASTE]
Return a table with source term, context sentence, suggested target term, confidence, and questions to confirm with the client.

Turn these approved terms into a glossary QA table.
Terms: [PASTE]
Return: source term, target term, language pair, context, forbidden alternatives, and client confirmation status.

Generate a client terminology system from these prior translations.
Source/target samples: [PASTE]
Return: glossary entries, usage notes, do-not-translate terms, style rules, and QA prompts for future jobs.

Design a terminology governance workflow for translation clients. Include: intake, approved sources, term owner, review cadence, conflict rules, export format, QA checks, and client approval notes.

Localize this copy for [TARGET LOCALE].
Source: [PASTE]
Brand voice: [PASTE]
Return: 3 localized variants, literal back-translation, cultural notes, risky phrases, and terms to verify.

Review this localized marketing copy against the glossary.
Glossary: [PASTE]
Localized copy: [PASTE]
Return: term mismatches, tone issues, cultural concerns, unclear claims, and suggested edits.

Create a localization packet.
Source campaign: [PASTE]
Target locale: [PASTE]
Return: locale brief, localized message, website variant, ad variant, email variant, cultural risks, and review checklist.

Create a localization QA playbook. Include: required inputs, locale brief, glossary rules, cultural review, claim verification, channel adaptation rules, and final approval checklist.

QA this translation against the source.
Source: [PASTE]
Translation: [PASTE]
Return: mistranslations, omissions, additions, register/tone issues, terminology issues, formatting issues, and severity level for each.

Create a translation QA checklist for this client.
Style guide: [PASTE]
Glossary: [PASTE]
Return checks for terminology, tone, names, numbers, dates, formatting, omissions, and client-specific rules.

Back-translate this target text into [SOURCE LANGUAGE], compare it to the original, and create an issue table.
Original: [PASTE]
Target: [PASTE]
Return: differences, likely cause, severity, and suggested human-review action.

Create a translation QA severity rubric. Include: critical, major, minor, and preference issues; examples; reviewer actions; client escalation rules; and final delivery checklist.

Create interpreter briefing notes.
Agenda/materials: [PASTE]
Languages: [LANGUAGE PAIR]
Return: topic summary, speaker names, likely terminology, acronyms, sensitive topics, and questions to ask the client.

Build a session glossary from these materials.
Materials: [PASTE]
Return: source term, target term, context, pronunciation notes if useful, and terms requiring client confirmation.

Create a full interpreter prep packet.
Materials: [PASTE]
Return: topic brief, terminology table, likely difficult segments, cultural notes, client clarification questions, and confidentiality reminder.

Create interpreter prep checklists by assignment type. Include: intake questions, confidentiality risks, topic research, terminology, speaker names, logistics, boundaries for AI use, and post-session notes.