50 ChatGPT & AI Prompts Every Technical Program Manager Should Use

As a Technical Program Manager (TPM), you’re at the intersection of engineering, product, and execution—juggling fast-paced projects, high expectations, and constant context-switching. It’s a demanding role that requires clarity, efficiency, and strategic thinking at every step.

Chances are, you’ve already used AI to capture meeting notes or draft emails.

But what if AI could take on even more of the mental load involved in managing programs?

In this post, you’ll find 50 carefully crafted AI prompts tailored specifically for TPMs. These prompts are designed to streamline your program management workflows, enhance communication, and help you tackle real-world challenges, whether it’s drafting stakeholder updates, creating Jira epics, or identifying project risks. Let AI do the hard work, so you can work smart, and focus on driving impact where it matters most.

How to Use These AI Prompts

The AI prompts listed below work across modern generative AI tools—but not every tool excels at the same things. Use the tips below to get the most value from each prompt:

1. Pick the Right Tool for the Job

Before you drop in a prompt, choose the tool that fits the task best:

• ChatGPT is best for structured writing, idea generation, and iteration on tone and format. It’s also great for "thinking out loud" workflows where you want to refine ideas together.

• Claude excels with long-form input and higher-context understanding, especially when you need to process nuance across big text blocks, like PRDs or strategy docs.

• Perplexity is your go-to for researching real-time data, trends, or citationspulling from live sources like blogs, papers, and forums. Think of it as Google, but generative.

• Gemini integrates smoothly into your daily Google Workspace tasks. Gemini’s strength is in context-aware productivity.

  
  

Choosing the right AI assistant ensures the prompt lands well and saves you time.

Task	Best Tool
Drafting program updates	ChatGPT / Claude
Researching new technologies	Perplexity
Summarizing long docs	Claude
Email follow-ups	Gemini
Creating structured plans	ChatGPT

2. Be Specific

Give the model enough context to work with—project names, stakeholder goals, or technical details. A vague prompt leads to vague output.

3. Iterate, Don’t Automate

AI is most effective when you treat it like a creative partner. Use follow-up prompts to improve tone, clarity, or structure.

4. Adapt to Your Org’s Style

Every company has its own language, tone, and format. Prompt the AI to “Write in the style of our usual program reviews” or “Use our Slack-friendly tone.”

50 AI Prompts for TPMs

Here are 50 copy-ready, context-rich AI prompts categorized to support every stage of the product development lifecycle.

Strategic Planning & Roadmapping

1. Generate a one-page roadmap for [PRODUCT_NAME] based on these themes: [LIST_THEMES]. Organize by quarter and highlight any major dependencies or risks.

   Best for: ChatGPT, Claude

2. You are a TPM working with both infra and product teams. Help me translate this strategic initiative into an executable roadmap with milestones and success metric. Initiative: [PASTE_INITIATIVE_DESCRIPTION].

   Best for: Claude

3. Given this product vision: [PASTE_TEXT], suggest 3 roadmap options with different levels of risk and investment (low, medium, high).

   Best for: ChatGPT

4. Evaluate the alignment of this roadmap with our company OKRs: [PASTE_OKRs]. Highlight any gaps or conflicts.

   Best for: Claude

5. Draft a narrative-style roadmap overview (Amazon-style 6-pager) for [PRODUCT_OR_PLATFORM], aimed at leadership. Include context, goals, timeline, and trade-offs.

   Best for: Claude

6. Turn this messy brainstorming output into a clean, themed roadmap: [PASTE_NOTES]. Add logical groupings and rough timelines.

   Best for: ChatGPT

   
   

Prioritization & Estimation

7. You are facilitating a prioritization session. Use the RICE framework to score the following list of features: [PASTE_FEATURES_LIST]. Return a ranked list.

   Best for: ChatGPT

8. Compare and contrast MoSCoW and Kano prioritization methods. Which is better suited for [TEAM_CONTEXT]?

   Best for: Perplexity, Claude

9. Estimate team effort for these backlog items using t-shirt sizing. Add reasoning for each size. Items: [LIST_ITEMS].

   Best for: ChatGPT

10. Summarize the trade-offs between prioritizing [INITIATIVE_A] and [INITIATIVE_B] in terms of risk, engineering effort, and business impact.

    Best for: Claude

11. You’re preparing for a product planning session. Suggest 3 prioritization frameworks that balance short-term velocity and long-term platform health.

    Best for: ChatGPT, Claude

12. Create a structured estimation exercise agenda for cross-functional teams (engineering, design, product) for the following scope: [PASTE_SCOPE].

    Best for: ChatGPT

Dependency & Risk Management

13. Create a dependency map based on the following program components: [LIST_COMPONENTS]. Highlight any critical path items.

    Best for: ChatGPT

14. You are a TPM managing inter-team dependencies across infra and mobile. Draft a weekly update format to surface dependency risks early.

    Best for: ChatGPT, Claude

15. Given this list of dependencies and due dates, which ones are likely to become bottlenecks? Dependencies: [PASTE_LIST].

    Best for: Claude

16. Draft a message to [TEAM_NAME] explaining why their delay on [TASK] impacts overall launch readiness, using data from [DATA_SOURCE].

    Best for: ChatGPT

17. Simulate a pre-mortem for [PROJECT_NAME]. List 5 things that could go wrong, how they might happen, and how to mitigate each.

    Best for: ChatGPT, Claude

18. Summarize these risks into a table with columns: Risk, Likelihood, Impact, Mitigation Plan. Risks: [LIST_RISKS].

    Best for: Claude

Technical Understanding & Translation

19. Explain [TECH_CONCEPT] (e.g., Kafka, Kubernetes, vector DBs) in simple terms for a non-technical stakeholder. Use analogies.

    Best for: ChatGPT, Claude

20. You are a TPM reviewing this architecture doc: [PASTE_TEXT]. Summarize it into a TL;DR and flag areas needing clarity.

    Best for: Claude

21. Create a visual diagram (describe it in text) that represents the system design described here: [PASTE_ARCHITECTURE_DESCRIPTION].

    Best for: ChatGPT

22. Translate this technical proposal into a stakeholder-friendly one-pager for execs: [PASTE_TEXT]. Focus on outcomes and risks.

    Best for: Claude

23. You are preparing for a design review. Generate a checklist of questions a TPM should ask for this system: [PASTE_SYSTEM_DESCRIPTION].

    Best for: ChatGPT

24. Based on this GitHub repo or design doc, what are the likely integration risks? [LINK_OR_TEXT].

    Best for: Claude, Perplexity

Program & Project Execution

25. Create a RACI chart for the following cross-functional initiative: [PASTE_SCOPE]. Include roles like Eng, PM, Design, TPM.

    Best for: ChatGPT

26. Draft Jira epics and user stories from this project scope: [PASTE_DESCRIPTION]. Follow agile best practices.

    Best for: ChatGPT

27. Generate a weekly status update for [PROJECT_NAME], using these data points: [PASTE_DATA]. Highlight progress, blockers, and next steps.

    Best for: ChatGPT, Claude

28. Create a project plan with milestones, deliverables, and checkpoints for this goal: [PASTE_GOAL]. Use a table format.

    Best for: ChatGPT

29. Identify 3 delivery risks for this initiative and suggest mitigation actions. Initiative: [PASTE_DESCRIPTION].

    Best for: Claude

30. You are onboarding a new team into a live program. Generate a “TPM starter pack” summary that covers context, goals, workflows, and tools.

    Best for: ChatGPT

Stakeholder Communication

31. Draft a Slack summary for this 60-minute meeting. Keep it concise, clear, and action-oriented. Notes: [PASTE_NOTES].

    Best for: ChatGPT

32. You are writing a weekly update email to leadership for [PROGRAM_NAME]. Structure it using: TL;DR, Wins, Risks, Next Steps. Input: [PASTE_DETAILS].

    Best for: ChatGPT

33. Create a talking points doc for an upcoming check-in with [STAKEHOLDER_NAME]. Context: [PASTE_CONTEXT]. Include questions to ask.

    Best for: ChatGPT

34. Rewrite this program update to be exec-ready—shorter, sharper, with a strategic focus. Input: [PASTE_TEXT].

    Best for: ChatGPT, Claude

35. You’re prepping for a cross-functional sync. Generate a quick slide outline using this agenda: [PASTE_AGENDA].

    Best for: ChatGPT

36. Suggest a neutral way to communicate a timeline slip to external stakeholders, while maintaining credibility. Context: [PASTE_DETAILS].

    Best for: ChatGPT, Claude

Meeting Preparation & Follow-Up

37. Create an agenda for a design review with infra and product teams. Focus: [PASTE_TOPIC]. Include goals and key questions.

    Best for: ChatGPT

38. Summarize these meeting notes into a decision log: who decided what, when, and why. Notes: [PASTE_TEXT].

    Best for: Claude

39. Generate action items from this meeting transcript. Include owner and due date. Transcript: [PASTE_NOTES].

    Best for: Claude

40. Write a follow-up email after this stakeholder review. Include appreciation, key takeaways, and next steps. Meeting notes: [PASTE_NOTES].

    Best for: ChatGPT

41. You’re leading a post-mortem. Suggest a template and questions to guide a blameless retrospective for [PROJECT_NAME].

    Best for: ChatGPT

42. Turn these raw whiteboard notes into a structured summary for the team: [PASTE_TEXT].

    Best for: ChatGPT

Launch Readiness & Reporting

43. Create a launch checklist for [PRODUCT_FEATURE], including tech readiness, go-to-market steps, and rollback plans.

    Best for: ChatGPT

44. You are prepping for a launch go/no-go meeting. Generate a summary slide covering: status, open risks, and key metrics.

    Best for: ChatGPT

45. Based on this rollout plan, what metrics should we track post-launch to assess success? Plan: [PASTE_TEXT].

    Best for: Claude

46. Draft an internal launch announcement for [PRODUCT_NAME]. Audience: [ENGINEERING_TEAM | SALES | EXEC TEAM].

    Best for: ChatGPT

47. Generate a risk matrix for this upcoming release: [PASTE_SCOPE]. Format with Likelihood x Impact.

    Best for: ChatGPT, Claude

48. Simulate a “Day 1 issues” post for launch day. What could go wrong and how would we triage? Input: [PRODUCT_FEATURE].

    Best for: ChatGPT

49. Draft a final launch retrospective summary with key wins, challenges, and lessons learned. Input: [PASTE_DETAILS].

    Best for: ChatGPT, Claude

50. Write a data-driven post-launch report highlighting engagement, uptime, and bug rate. Include visual suggestions.

    Best for: Claude

Example Outputs

Here are two example outputs from the above list of prompts

Prompt 27: Weekly Status Update

“Generate a weekly status update for [PROJECT_NAME], using these data points: [PASTE_DATA]. Highlight progress, blockers, and next steps.”

Project: Infra Migration  
Progress: Backend team migrated 80% of endpoints to new system  
Blocker: Frontend team is delayed due to missing API documentation  Next Step: Align on API contracts, finalize migration by next Friday

Example Output:

Weekly Status Update – Infra Migration

Progress: Backend team has completed ~80% of endpoint migration Core services now running on the new infra stack with no major issues

Blockers: Frontend team is blocked on undocumented API changes; coordination needed

Next Steps: Sync between frontend/backend leads to finalize API contracts Targeting full migration completion by next Friday

Prompt 19: Explain a Technical Concept Simply

“Explain [TECH_CONCEPT] in simple terms for a non-technical stakeholder. Use analogies.

Tech Concept: Kafka stream processing
Audience: Sales and marketing stakeholders

Example Output:

Explaining Kafka Stream Processing (Non-Technical Version):

Think of Kafka like a real-time conveyor belt for data. Imagine orders being placed on an e-commerce site. Each new order is a “package” placed on this conveyor belt.

Instead of waiting to collect all orders at the end of the day, Kafka lets us process each order as soon as it hits the belt.

So if a customer places an order, the system can immediately check inventory, charge the card, and send a confirmation—all while new orders keep flowing in.

This real-time flow is what helps product teams make faster decisions, detect issues earlier, and power live dashboards or features like “people also bought…”

Pro Tips for Crafting Your Own Prompts

The 50 prompts we’ve provided are a powerful starting point—but the real magic happens when you start crafting your own. Prompt engineering is a high-leverage skill for TPMs, especially when you're juggling multiple stakeholders, tight timelines, and technical ambiguity.

Here’s how to build high-quality, task-specific prompts that actually get results::

1. Start with Role, Context & Intent

AI responds better when it understands who it’s helping and the context it’s working within.

Strong Prompt: “You are a senior TPM at a B2B SaaS company preparing a QBR deck. Draft a slide that summarizes roadmap status across three cross-functional teams.”

Why it works: This prompt gives role, domain, audience, and task—critical details for producing precise, effective results.

Weak Prompt: “Summarize our roadmap.”

Why it falls short: It lacks the context needed for meaningful output.

Think of prompting as briefing an eager junior colleague. You wouldn’t set them up for success with vague instructions. Instead, empower them with context:

• Role: Who they’re stepping into

• Domain The specific expertise required

• Audience: Who they’re addressing

• Task: The clear objective

2. Stack Prompts for Complex Deliverables

Break large or fuzzy tasks into smaller, modular prompt chains:

• Prompt 1: Draft a raw version

• Prompt 2: Refine tone for execs

• Prompt 3: Reformat as a one-pager or table

• Prompt 4: Suggest potential objections or gaps

Example: “Take this sprint update → improve clarity → make it non-technical → turn into a 3-slide deck.”

This mimics how TPMs already think—refining, simplifying, adapting—now just with AI in the loop.

3. Use Constraints to Direct Output

The more guardrails you give, the sharper the result.

Include instructions like:

• “Keep it under 100 words”

• “Use 5 bullet points”

• “Write it in a customer-facing tone”

• “Format as a Slack message”

• “Make it suitable for a VP with limited technical background”

Pro Tip: Constraints are especially useful for exec comms, Jira ticket writing, or PRD summaries, where brevity, clarity, and tone really matter.

4. Iterate Like You Would With a Human

Don’t expect the first result to be perfect. Ask follow-up questions, give feedback, and shape the response.

Example:“Good start—now make it more concise and remove passive voice.”“Add a call to action at the end.”“Give me two alternative phrasings.”

Think of prompting as a conversation, not a command.

5. Use Templates for Repeated Prompts

If you find yourself writing the same types of prompts over and over—status updates, retros, risk assessments—turn them into reusable templates:

Example: “You are a TPM. Given [milestone], [risk], and [impact], draft a risk update using this structure: [X].”

This turns AI into a repeatable, reliable tool—not just a novelty.

Bottom Line: AI is Your Thinking Partner

Prompting is not about writing fancy commands. It’s about structuring your thoughts clearly enough for AI to contribute meaningfully. As a TPM, this mirrors your core skillset—clarifying complexity, structuring ambiguity, and delivering clarity at speed.

Generative AI is redefining how TPMs work. It’s not just about producitivity hacks anymore. It's about working smarter, and building a sustainable TPM career that can outlast the impact of AI on jobs. With the right use of AI prompts, you can:

• Improve stakeholder communication

• Make faster decisions

• Write clearer specs

• And scale your thinking with less effort

These 50 AI prompts for TPMs are just a starting point. Use them as-is, remix them, or evolve them to fit your org. And if you develop something great, share it with the TPM Academy community.

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Want to Download Even More Prompts?

We’ve bundled all these 50 prompts plus created advanced prompts for complex scenarios, and even added a prompt engineering guide in our FREE TPM AI Prompts Toolkit.

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Frequently Asked Questions (FAQs)

What is prompt engineering?

Prompt engineering is the process of crafting clear, structured inputs (called prompts) that guide AI tools like ChatGPT or Claude to produce high-quality, relevant output. For Technical Program Managers (TPMs), prompt engineering involves framing questions or tasks in a way that aligns with your role, context, and goals—like generating stakeholder updates, summarizing design docs, or creating roadmaps. It’s a modern skill that helps TPMs turn AI into a productive, collaborative partner.

Is prompt engineering a useful skill for TPMs?

What are the best AI prompts for Technical Program Managers?

How can TPMs use ChatGPT or AI tools in their daily work?

Can AI help with stakeholder communication and program updates?

Which AI tool is best for TPMs: ChatGPT, Claude, Perplexity, or Gemini?

Are there templates or toolkits for AI prompt usage in TPM work?