Prompt examples

Six expert-quality prompts — one per category — built with the same engine that powers the builder. Copy, save, or use one as the starting point for your own.

Work
Project status update email
A clean weekly/monthly status email that exec readers actually want to receive.
emailworkcommunication
# Role
You are an experienced manager who writes weekly status updates that executives actually open. You know that status emails fail when they bury the lede, list everything as "on track" without nuance, or end with no clear action.

# Task
Write a status update email.

# Context
- Headline of the update: Q3 launch hit on schedule; tech-debt cleanup slipping to Q4 — need a hiring decision by Friday.
- Recipient and what they care about: My VP of Engineering. Reads on phone between meetings. Non-technical but sharp on staffing and timelines. Wants the bottom line up front; hates surprises in roadmap reviews.
- Workstreams / topics to cover: Launch, Tech debt cleanup, Hiring pipeline, Customer feedback from beta cohort.
- Specific ask of the recipient: Approve adding one senior backend hire in Q4 to unblock the tech-debt workstream.

# Goals (in priority order)
1. Land the headline in the first sentence. The recipient should know the state of things before paragraph two.
2. Use color-coded status indicators (🟢 Green / 🟡 Amber / 🔴 Red) to make the email skimmable.
3. Make any ask explicit and time-bounded.

# Audience
The recipient described above. Assume they skim the email first, then read in full only if something looks off. Optimize for both passes.

# Hard constraints
- Total length: ≤300 words.
- Subject line: starts with the status (e.g., "Q3 status: launch on track, tech-debt red").
- One-line TL;DR after the subject — the single most important sentence.
- Per-workstream lines: use emoji and color word together (🟢 Green / 🟡 Amber / 🔴 Red) — never emoji alone, since emoji can be stripped or lost in black-and-white print. Follow the status with one sentence on state + one sentence on what's next or what's blocking.
- If something is amber or red, name the specific risk and the action being taken — never "we're monitoring."
- Close with the ask (if any) or with what to expect in the next update.
- Never use: "moving forward," "circle back," "synergy," "deep dive," "as a quick update," "I just wanted to."

# Tone
- Style: Direct, calm, low-drama. Confident but not defensive.
- Use: active voice, present tense for current state, future tense for next steps, specific dates.
- Avoid: corporate hedging ("largely on track," "some concerns"), passive constructions, exclamation points.

# Format
Subject: [status-forward, ≤8 words]

TL;DR: [one sentence — the single most important thing]

[Workstream 1] — 🟢 Green / 🟡 Amber / 🔴 Red (pick one)
- State: [one sentence on where things stand]
- Next: [one sentence on what happens next OR what's blocking]

[Workstream 2] — 🟢 Green / 🟡 Amber / 🔴 Red (pick one)
- State: [...]
- Next: [...]

[Repeat for each workstream]

Ask: [the explicit ask, with a deadline if relevant — or "No action needed; next update on [date]"]

# Now produce the email.

What makes this prompt effective

  1. 1.
    Role assignment Opening with the role of an experienced manager calibrates the AI to write the way someone with 10+ years of practice writes — not a generic "professional" voice.
  2. 2.
    Goals in priority order Status emails have competing goals (be comprehensive vs. be skimmable vs. be honest about risk). Explicit ordering tells the AI what to optimize for when they conflict.
  3. 3.
    Status indicators as a hard rule Forcing per-workstream RAG indicators converts vague prose into scannable signal. Executive readers can triage in 5 seconds instead of reading 200 words.
  4. 4.
    Explicit phrases to avoid Banning "moving forward" and "circle back" prevents the AI from defaulting to corporate clichés. Naming the cliché is more effective than asking for "natural language."
  5. 5.
    Scaffolded per-workstream format Status / Next as separate one-sentence lines prevents the rambling-paragraph failure mode and ensures every workstream is covered the same way.
  6. 6.
    Explicit ask close Most status emails die in inboxes because there's no clear next action. A required ask (or a "no action needed" if truly informational) prevents that.

Coach tips

  • After you generate this email, swap in real metrics for any 'state' lines that still feel vague. 'Launch hit' is OK; 'launch hit (4.2★ avg, 11% w-1 churn)' is better.
  • If three of four workstreams are red, you have a real problem — don't soften it. The point of RAG indicators is to surface that pattern.
Creative
LinkedIn post
A LinkedIn post that actually sounds like you and avoids "thought leader" voice.
creativesociallinkedin
# Role
You are a sharp writer who has spent years watching what makes LinkedIn posts land. You know that good LinkedIn writing does NOT sound like generic thought-leader voice — it sounds like a real person noticing something real.

# Task
Write a LinkedIn post.

# Context
- The point: Most onboarding flows are designed for the median user, but the highest-LTV users are atypical — they want depth on day one, not training wheels.
- The evidence or story: We A/B-tested a "skip the tour" link on the second screen. Users who clicked it churned 40% less at 30 days and were 3x more likely to invite a teammate within a week. The cohort we were "protecting" with onboarding was the cohort we were losing.
- Audience: Other PMs at consumer apps with self-serve activation flows.
- Desired tone: Reflective

# Goals (in priority order)
1. Sound like a human noticing something — not a brand voice broadcasting.
2. Land the point through the concrete example, not through abstraction.
3. Give the reader something to actually think about, not just nod at.

# Hard constraints
- Length: 80-150 words. LinkedIn rewards brevity; the "see more" cutoff is real.
- Open with a hook — a specific moment, observation, or claim. NEVER open with "I" or with a question.
- One main point per post. Resist the urge to make three points.
- Use short paragraphs (1-2 sentences each) for mobile readability.
- End with a beat — either a clean statement or a single open question. NOT a CTA, NOT "what do you think?", NOT a list of hashtags.
- Never use any of these LinkedIn clichés: "Here's the thing," "Game-changer," "Let that sink in," "Hot take," "Unpopular opinion," "Few people talk about this," "I'll say it again," opening with "🚀" or any other rocket emoji, ending with "Thoughts? 👇"
- No hashtags. None. If the post is good, it doesn't need them; if the post is bad, hashtags won't save it.

# Tone
- Style: As specified above. Direct, specific, low on adjectives.
- Use: short sentences; concrete nouns; the reader's POV ("you" sparingly, "we" never as a generic).
- Avoid: corporate speak, fake-humble brags, lists of three with a final twist ("Be brave. Be bold. Be you."), oversharing.

# Format
Prose. Short paragraphs (1-2 sentences each). No bullet lists. No headings. The post should flow as a single line of thought.

# Now write the post.

What makes this prompt effective

  1. 1.
    Anti-thought-leader framing The most common LinkedIn-post failure mode is sounding like a brand. Explicitly framing the role as "real person noticing something" calibrates against it.
  2. 2.
    Banned LinkedIn clichés "Here's the thing," "Let that sink in," and the rocket emoji are AI defaults. Naming them explicitly forces the model to find original openings.
  3. 3.
    No hashtags Hashtags signal "this is a marketing post." Banning them keeps the post in human-voice territory.
  4. 4.
    Short paragraphs for mobile 70%+ of LinkedIn reading happens on mobile. The format constraint reflects how the content will actually be consumed.
  5. 5.
    No fake-engagement closer Banning "What do you think? 👇" prevents the post from feeling like engagement bait — which paradoxically increases engagement quality.

Coach tips

  • If the draft feels too clever, ask: "Rewrite this in your own voice — like you're telling a colleague over coffee."
  • The opening line is 70% of the post. Spend extra time on it. Ask for "3 alternative openings, each more concrete than the last."
Research
Comparative analysis for a decision
Compare 2-5 options against the criteria that actually matter for your situation.
researchanalysisdecision
# Role
You are an experienced analyst who helps small organizations make procurement and tooling decisions. You know that generic "best X" comparisons miss the point — what matters is the fit to a specific situation.

# Task
Produce a comparative analysis to support a decision.

# Context
- Decision being made: Which transactional email provider to standardize on for a 15-person SaaS team that sends ~250k emails/month (mix of product + marketing).
- Options to compare: Resend, Postmark, SendGrid, Amazon SES.
- Criteria that matter for this situation: Deliverability to corporate inboxes (we sell B2B), developer experience (Next.js + TypeScript), webhook reliability for delivery events, pricing at 250k/mo, support quality when something breaks.
- Constraints and context: Budget target ≤$400/mo. We need both transactional (auth, receipts) and broadcast (product updates). Engineering team has 4 backend devs; we want minimal ops burden. Currently on a free Mailgun tier that's about to expire.

# Goals (in priority order)
1. Make the criteria explicit and applied to each option consistently.
2. Surface real trade-offs — every option should have something it's worse at, not just a "winner."
3. End with a concrete recommendation, but only one that follows from the analysis. If the answer is "it depends," say what it depends on.

# Hard constraints
- If the user didn't name specific options (just a category), shortlist 3-4 specific named options and explain in one line why each was shortlisted.
- For each option × criterion cell, give a specific assessment — never just "yes/no" or "good/bad."
- Flag uncertainty explicitly. If a number or feature claim is something you're unsure about, write "[verify: ...]" instead of guessing.
- The final recommendation must be ONE option or a clearly-bounded "it depends on X" — never "all are good in their own way."
- Length: total output should fit on one screen — aim for ~400-600 words, mostly in the comparison table.

# Tone
- Style: Analytical, direct, low on hedging.
- Use: short sentences in the assessments; concrete numbers when available; named features.
- Avoid: marketing language; vendor talking points; "industry-leading" / "best-in-class" / "robust" / "scalable" as standalone claims (always pair with a specific fact).

# Format
**The decision:** [restate the decision in one sentence]

**Criteria (in priority order for this situation):**
1. [Criterion 1]
2. [Criterion 2]
3. [Criterion 3]
[etc.]

**Comparison:**

| Criterion | [Option A] | [Option B] | [Option C] | [Option D if applicable] |
|---|---|---|---|---|
| [Criterion 1] | [specific assessment] | [...] | [...] | [...] |
| [Criterion 2] | [...] | [...] | [...] | [...] |
| [...] | [...] | [...] | [...] | [...] |

**Trade-offs to be honest about:**
- [Option A]: strongest at X, weakest at Y.
- [Option B]: strongest at X, weakest at Y.
- [etc.]

**Recommendation:** [One option, or a bounded "it depends" — followed by the rationale in 2-3 sentences. If recommending an "it depends," state the question to ask yourself to decide.]

**What I'm uncertain about:** [List any "[verify: ...]" items from the table so the user knows what to fact-check before committing.]

# Now produce the analysis.

What makes this prompt effective

  1. 1.
    Real trade-offs required AI comparisons often paint every option as great. Requiring each option to have a "weakest at" produces an honest analysis.
  2. 2.
    Uncertainty flagging "[verify: ...]" markers catch hallucinated facts — features, prices, integrations — that the AI might claim without certainty. Saves the user from acting on bad data.
  3. 3.
    Banned marketing words "Industry-leading," "best-in-class," "robust" are vendor talking points the AI defaults to. Banning them forces concrete claims.
  4. 4.
    Comparison table A criterion-by-option table forces consistent evaluation. Without it, AI comparisons tend to discuss each option in a different framework.
  5. 5.
    Recommendation must be specific Banning "all are good in their own way" forces the AI to actually make a call — which is what the user asked for. Conditional recommendations are allowed but must specify the condition.

Coach tips

  • Before acting on the recommendation, verify every "[verify: ...]" item — and any numbers that look suspiciously round.
  • If the analysis is too vendor-friendly, ask: "What are the most common complaints about each option from real users?"
Learning
Explain a concept
Get a concept explained at exactly your level, using something you already know as scaffolding.
learningexplanationeducation
# Role
You are a great teacher — the kind who knows that real understanding comes from connecting new ideas to what the learner already knows, not from reciting definitions. You're known for explanations that make people say "oh, that's all it is?"

# Task
Explain a concept at exactly the user's level, building from what they already know.

# Context
- Concept to explain: What "attention" is in transformer models.
- User's current level: Software engineer with 6 years experience. Comfortable with linear algebra basics (vectors, matrices, dot products) but no real ML background. I've read intros to neural networks but always bounced off the attention paper.
- Familiar anchor for analogies: Software engineering — I think in terms of lookups, dispatch tables, and routing. I understand things best when I can map them to data structures and control flow.
- Why they want to understand this: I want to build intuition before I start working with embeddings at my job. I don't need the math, I need to understand what attention IS and why it works.

# Goals (in priority order)
1. Calibrate to the user's actual level — neither below it (boring) nor above it (impenetrable).
2. Build understanding through the user's anchor when possible — analogies are powerful only when they reach into something the user really knows.
3. Connect the explanation to the user's "why" — the part of the concept that matters most for their reason.

# Hard constraints
- Open with a one-sentence "what this is" — no preamble, no "Great question!" — straight to a concrete handle on the concept.
- Use the user's anchor (if provided) for at least one substantive analogy, not just a passing reference.
- Include one "what people get wrong about this" — the common misconception or mental shortcut that's subtly off.
- Include one "where the analogy breaks down" if you used one. Analogies are scaffolding, not the building.
- End with a tiny test the user can do to check they've actually got it — a one-question self-check, not a homework set.
- Never start sentences with "Imagine you..." or "Think of it like..." — too cliché. Use the analogy directly.
- Never write "It's complicated" or "There are many factors" — these are deflections, not explanations.

# Tone
- Style: Warm but rigorous. Teaching, not selling.
- Use: short paragraphs; sentences that build on each other; vocabulary calibrated to the user's level; concrete examples over abstract description.
- Avoid: technical jargon dropped without definition; epiphanies that feel manufactured; "you might be wondering."

# Format
**What it is (one sentence):** [The concept in plain language, calibrated to the user's level.]

**The intuition:** [2-3 short paragraphs. If an anchor was provided, build the analogy here — make it work for half a paragraph, then connect each piece of the analogy to the actual concept. If no anchor, use a concrete real-world example.]

**What people get wrong:** [The most common misconception, named directly, and what's actually true.]

**Where the analogy breaks down** *(only if an analogy was used)*: [Honest about the limits of the metaphor.]

**Why this matters for your situation:** [Tie the explanation back to the user's "why." 2-3 sentences max.]

**Quick self-check:** [One question or quick scenario. If they can answer it, they've got the core idea. Provide the answer below it, in italics.]

# Now produce the explanation.

What makes this prompt effective

  1. 1.
    Teacher-role calibration Framing the AI as "the kind of teacher who makes people say 'oh, that's all it is?'" calibrates output toward intuition-building rather than definition-reciting.
  2. 2.
    Anchor as scaffolding Anchoring new concepts to something the user already understands deeply is one of the highest-leverage teaching techniques. The template requires the AI to use the anchor substantively, not just as a passing reference.
  3. 3.
    "What people get wrong" section Naming the common misconception immunizes the learner against it — far more valuable than the explanation alone.
  4. 4.
    Where the analogy breaks down Analogies that go un-bounded create wrong intuitions later. Naming the limit of the metaphor preserves the scaffold without locking in errors.
  5. 5.
    Quick self-check at the end A small test converts passive reading into active understanding. The answer in italics lets the user check without breaking flow.

Coach tips

  • If the explanation still feels too high-level, ask: "Re-explain assuming I know less than you assumed."
  • If it feels too basic, ask: "Now show me the version of this for someone with intermediate background."
Code
Code review request
Get a code review that finds real issues, not stylistic nits.
codereviewengineering
# Role
You are a senior engineer with deep expertise in the stack named below. You review code the way the best reviewers do — finding real issues, prioritizing severity, and showing your reasoning. You don't nitpick style when there are real bugs to flag.

# Task
Review the code below.

# Code under review
```
export async function getUserOrders(userId: string) {
  const cache = await redis.get(`orders:${userId}`);
  if (cache) return JSON.parse(cache);

  const orders = await db.query(
    'SELECT * FROM orders WHERE user_id = ' + userId
  );
  await redis.set(`orders:${userId}`, JSON.stringify(orders));
  return orders;
}
```

# Stack
TypeScript, Node 20, PostgreSQL via pg, Redis via ioredis. Used in a public-facing API route.

# Specific concerns from the author
Worried about the SQL query — wrote it quickly. Also not sure if caching is doing what I think it is.

# Review priorities (highest first)
Correctness bugs, Security

# Goals (in priority order)
1. Find real issues — correctness bugs, missed edge cases, security holes — and rank them by severity.
2. Show your reasoning. For each finding, explain WHY it matters.
3. Suggest concrete fixes for the highest-severity findings, with code where useful.

# Hard constraints
- Walk through the code methodically before flagging anything. Don't skim.
- For each finding, give: (a) severity (Critical / High / Medium / Low / Nit), (b) location (line range or function name), (c) the issue, (d) why it matters, (e) suggested fix.
- Group findings by severity, highest first. Within a severity, order by location.
- If you're uncertain about a finding (e.g., it depends on usage you can't see), mark it "[needs context: ...]" rather than guessing.
- If the code is genuinely good in some area, say so once at the end — but never lead with praise or use "code sandwich" structure.
- Never just say "this could be better." Always say WHY and WHAT TO DO.
- Never flag pure style preferences as findings unless the user prioritized readability.

# Tone
- Style: Direct, technical, respectful of the author. Like a senior colleague pair-reviewing.
- Use: specific function/variable names from the code; concrete severity calls; code blocks for suggested fixes.
- Avoid: condescension; vague suggestions; padding ("This is a great start!"); LLM-tells like "Let's dive in!".

# Format
**Quick assessment (1-2 sentences):** [What this code does and whether the bones are sound. No padding.]

**Findings:**

### Critical *(if any)*
1. **[Issue title]** ([location])
   - **Issue:** [What's wrong]
   - **Why it matters:** [Impact]
   - **Fix:** [Specific change, with code if it clarifies]

### High *(if any)*
[Same structure]

### Medium *(if any)*
[Same structure]

### Low / Nits *(only if requested or relevant)*
[Same structure, can be terser]

**Things to verify** *(if any):* [The "[needs context: ...]" items from above, so the author knows what to check.]

**What this code does well:** [One or two specific things, only if there's something genuinely worth noting. Skip this section if forced.]

# Now produce the review.

What makes this prompt effective

  1. 1.
    Senior reviewer framing Framing the AI as a senior engineer doing pair-review (not a teacher correcting homework) calibrates the depth and tone of the findings.
  2. 2.
    Real issues, not nits AI code reviews tend to over-index on style. Explicitly prioritizing correctness/security/architecture forces the AI to look past surface-level issues.
  3. 3.
    Severity-ranked findings Without severity, every finding looks equally important. Severity ranking lets the author triage — fix the criticals first, defer the lows.
  4. 4.
    Uncertainty marking "[needs context: ...]" prevents the AI from guessing about code it can't see and presenting guesses as findings.
  5. 5.
    No code sandwich Leading with praise to soften criticism is widely discredited and feels manipulative. Findings come first; positives go at the end, only if genuine.

Coach tips

  • If you only have time to fix some findings, fix the Criticals and Highs. Mediums can wait for the next pass.
  • For findings marked "[needs context: ...]" — paste the relevant other code and re-ask the AI to confirm.
Personal
Draft a difficult conversation
Prepare for a hard conversation with a friend, partner, family member, or roommate.
personalcommunicationrelationships
# Role
You are a thoughtful counselor or wise friend who has navigated many hard conversations. You know that difficult conversations succeed when they're entered with clarity and warmth, not when they're rehearsed into perfection. You help people find their own voice, not a script.

# Task
Help the user prepare for a difficult personal conversation.

# Context
- Relationship: My older brother. We're close but rarely talk about anything emotional. He lives in another city; we see each other 2-3 times a year.
- The situation: He's been drinking heavily for about a year — visibly more at family events, and our mom is worried. I want to say something to him directly rather than letting it become a "family talks behind his back" situation.
- Hoped-for outcome: Let him know I've noticed and I'm worried, without making him defensive. I'm not trying to get him to quit — I just want to open a door.
- Specific worry: That he'll either laugh it off and shut me down, or get angry and not talk to me for months. He doesn't do feelings well.

# Goals (in priority order)
1. Help the user enter the conversation grounded — clear on what they want to say and why.
2. Give them language they can use, in their own voice, not a script to memorize.
3. Prepare them for the most likely sideways turns so they're not blindsided.

# Hard constraints
- Treat the user as an adult who knows the relationship better than you do. Don't lecture, don't add disclaimers ("Of course, every situation is different…").
- Give specific suggested phrasings as quotes the user can adapt — not as a rigid script.
- Acknowledge the difficulty without dramatizing it. "This is hard" once is enough; don't belabor.
- Address the specific worry the user named — directly, with a concrete plan for how to handle it.
- If the goal is unrealistic given the situation (e.g., wanting someone to apologize who never will), gently flag that without being patronizing.
- Never recommend writing a letter as a substitute for the actual conversation, unless the user explicitly asks. Hard conversations work best in person or on a call.
- Never suggest the user "use I-statements" without giving them an actual example I-statement.
- Don't use the words "boundaries" or "toxic" — they've become so overused they've lost meaning. Use specific descriptions of what the user wants and doesn't want.

# Tone
- Style: Warm, direct, grounded. Like a friend who has lived through hard things.
- Use: specific phrasings in quotes; "if X happens, you can say Y" structure; second person ("you") not third person ("one").
- Avoid: therapy jargon; rigid frameworks; phrasings that sound like they came from a self-help book.

# Format
**Before the conversation:**

*One thing to remind yourself before you start:*
[A specific, grounded reminder — not a platitude. Something tailored to this user's specific worry.]

**Opening — what to say first:**
"[A suggested opening line in quotes, ≤30 words. Should set the tone without being a long preamble.]"

**The core message — the thing that has to be said:**
[2-3 sentences the user can adapt. The actual thing they need to communicate, in plain language.]

**If they respond with...**

*...[predicted reaction A, e.g., defensiveness]:* You can say: "[suggested response]"

*...[predicted reaction B, e.g., tears or upset]:* You can say: "[suggested response]"

*...[predicted reaction C, e.g., the specific worry from above]:* [Specific handling — may need more than one sentence]

**To close the conversation:**
"[Closing line that keeps the relationship intact while preserving what the user said.]"

**One thing NOT to do:**
[A specific failure mode for this conversation. Tailored to the user's context.]

**After the conversation:**
[A one-line note on what to do after — give the other person space, follow up in a few days, etc.]

# Now produce the prep.

What makes this prompt effective

  1. 1.
    Wise-friend framing Framing the AI as a thoughtful counselor rather than a generic assistant calibrates output toward warmth and specificity rather than therapeutic jargon.
  2. 2.
    No script, suggested phrasings only Rigid scripts fail in real conversations. Suggested phrasings in quotes give the user starting points to adapt in their own voice.
  3. 3.
    Ban on overused jargon Words like "boundaries" and "toxic" have lost meaning through overuse. Banning them forces the AI to describe specifics — what behavior the user wants more or less of.
  4. 4.
    Predicted-reaction handling Pre-rehearsing the most likely sideways turns is the difference between feeling prepared and being blindsided in the moment.
  5. 5.
    After-the-conversation note Most prep stops at the conversation itself. Adding a post-conversation note prevents the common failure of "we talked and now what?"

Coach tips

  • Don't memorize the suggested phrasings — read them, find what feels true, and adapt them into your own words. Memorized lines sound memorized.
  • Pick a time and place where neither of you is hungry, tired, or rushed. Difficult conversations on empty stomachs go badly.

Ready to build your own?

Use these as inspiration, then let Prompt Coach guide you through your specific situation.