Mastering AI Prompts: How Product Managers Can Unlock AI's Potential

Artificial intelligence (AI) is more than just a buzzword––it’s a transformative tool that's reshaping how product managers work, strategize, and deliver value. Yet, for many, AI still feels like an enigmatic black box, like noise. It’s full of promise but difficult to use effectively and often fails to deliver. However, that’s not going to slow the pace of innovation. 

Over 75% of respondents in McKinsey’s 2025 State of AI Report now say that their organizations use AI in at least one business function. Usage will only increase as the technology evolves. Product managers will be expected to lead the way both in the products they create and in the ways they work. 

But, getting started with AI can feel like more work than its worth. While AI won’t replace product managers (this is our firm belief), product managers who know how to use AI will find themselves achieving, influencing, and ultimately executing more. It all begins with knowing the prompting basics. 

Why AI for Product Managers?

Product management is fundamentally about understanding user needs, coordinating complex development processes, and ultimately, building products that deliver real value for customers.

AI offers unprecedented capabilities to:

• Accelerate research and analysis by processing vast amounts of customer data in minutes
• Generate initial drafts of critical documents, like PRDs, with structured, coherent content
• Provide structured brainstorming support that challenges conventional thinking
• Simulate diverse user personas and scenario planning
• Augment decision-making with rapid information synthesis and insights generation
• Reduce administrative overhead, allowing more time for strategic thinking

Understanding Prompt Engineering: Your AI Superpower

Prompt engineering is the art and science of communicating with AI systems to extract maximum value. It's not about knowing complex coding or having a computer science degree—it's about learning to communicate clearly, contextually, and strategically.

Consider prompt engineering like learning a new language. The more precisely and thoughtfully you communicate, the better the response you'll receive. It's a skill that can be learned, practiced, and refined through consistent application and experimentation.

Crafting Effective AI Prompts: A Strategic Approach

Creating truly effective AI prompts requires understanding how to communicate strategically with these systems. Just as product development benefits from thoughtful planning, prompt design improves dramatically when we understand the underlying technology we're working with.

Understanding Large Language Models (LLMs)

Before diving into prompting, it's crucial to understand that AI models like ChatGPT are essentially advanced prediction engines. They generate responses based on patterns in their training data, which means the quality of your input and the knowledge of the model itself directly influences the quality of the output.

Think of LLMs as incredibly sophisticated translation machines—they're converting your textual input into the most statistically probable response based on their training. This means nuance, specificity, and context are key.

AI Prompting Best Practices

The best thing you can do is treat AI like a stranger. It doesn’t know who you are, what you do, or why what you’re asking matters. It has no reference for what you’re hoping to get out of it, so overexplain.

Follow these best practices to craft prompts that create actually useful outputs.

Make Implicit Context Explicit

AI doesn’t understand your background or objectives if you haven’t shared that information. It’s easy to write a simple task like “Write an email asking to reschedule tomorrow’s meeting.” But, you’re going to get a generic response. A better prompt would be to, “Write an email to my design partner, John, asking to reschedule our design review meeting from 9am tomorrow to some time on Friday.”

Provide clear, comprehensive context about your specific scenario. Include relevant background information that might not be immediately obvious. And if you’re still not getting the response you want, don't hesitate to ask the AI to clarify or request more information to help hone the response.

Leverage Examples

Adding examples can be a great way to reduce the need for context. With relevant, specific examples you can better guide the AI's understanding and get your desired output. For example, if you prefer a specific style or template of PRD, include that. Add in examples of successful PRDs. This will help the AI model create a more accurate output.

Embrace Iteration

Your first prompt is rarely perfect, so treat AI interactions as collaborative conversations. Refine and adjust based on initial responses. It’s easy to feel dejected or give up when the model isn’t achieving what we want it to, but instead think of each interaction as an opportunity to improve your prompting skills. With time and effort, your AI skills will grow alongside the technology’s capabilities. 

6 Key Prompt Components

A good AI prompt includes a mixture of these six components: 

1. A Task 
2. Context 
3. Examples 
4. Personas 
5. Formats
6. Tone

Not every prompt will use all six, but understanding them and being able to use them when necessary is critical to prompting successfully. 

1. Task

This is your base-level, what you want the model to perform. Start with an action-oriented verb and articulate your end goal.

Prompt Examples:

• "Generate a competitive analysis matrix."
• "Draft a product vision statement."
• "Analyze the attached user feedback and identify the top three pain points."

2. Context 

With context, you add nuance to the prompt which helps the model generate responses more aligned with your specific needs. 

Clarify the following: 

• Who is the intended audience of the output?
• What specific objectives am I pursuing?
• What relevant background information should be included?
• What constraints or parameters are important?

Prompt Example:

"We're ABC company, a B2B SaaS company offering project management software to mid-sized engineering firms. Our primary competitors are Asana, Monday.com, and ClickUp. We're considering adding AI-powered task automation features to differentiate ourselves in the market."

3. Examples

Provide concrete examples of your desired output, whether that’s copy pasting something in or uploading an image or PDF. This will help show the style, depth, and format you're seeking.

Prompt Example:

"Here's an example of a feature announcement email we've sent previously that resonated well with customers: [example text]. Please maintain a similar structure and tone while highlighting our new integration capabilities."

4. Persona

Define the "role" you want the AI to assume. While it may feel like you’re turning the AI into an actor, sharing the perspective you want to take can give them helpful context.

Prompt Examples: 

• "Respond as a senior product strategist with experience in SaaS pricing models."
• "Write this from the perspective of a user researcher who has conducted extensive interviews with our target demographic."

5. Format

Specify your desired output structure. Some of this may already be covered in your examples. Call out specifics here like bullet points, numbered lists, and paragraphs, as well indicating your preferred level of detail. For prompts asking for revisions to provided content, you can also instruct the model to bold its changes. 

Prompt Example:

"Present this as a one-page executive summary with three sections:

1. Market Opportunity
2. Competitive Landscape
3. Recommended Approach.

Include 3-5 bullet points per section, and highlight the most critical points in bold."

6. Tone

Finally, specify the emotional or professional tenor of the output. 

Prompt Examples:

• "Use a professional but conversational tone that would be appropriate for a team presentation."
• "Write with an authoritative, data-driven tone suitable for a board meeting."

Prompting Approaches: Beyond the Basics

Gathering all the necessary elements for a good prompt takes time. But, the real challenge is refining all of that information into a concise directive. If you overload the model, you’ll end up with a half-baked answer or a completely off-track response. In that instance, consider breaking up the flow of information with these different prompting tactics. 

Zero-Shot vs. Few-Shot Prompting

Instead of trying to get a perfect answer up front with one prompt, provide examples to guide the AI. Think of this as the difference between telling someone to "design a feature" versus showing them examples of what successful features look like in your product ecosystem.

• Zero-shot: Asking the AI to perform a task without specific examples.
• Few-shot: Providing 1-3 example responses to guide the AI's understanding.

Few-shot prompting can dramatically improve response quality and alignment with your expectations. For instance, if you were looking to populate requirements for a new in-app notification center, it could look something like the following.

Prompt Example: 

"Please write requirements for our new in-app notification center using the attached document. Here's an example of our standard requirement format:

EXAMPLE: 

• Feature: Dark Mode Implementation 
• User Need: Users need to reduce eye strain when using the app in low-light environments 
• Requirements: 
• System shall automatically detect device display settings and match theme accordingly 
• Users shall be able to manually override system settings via profile preferences
• All UI components must maintain WCAG AA contrast ratios in dark mode 
• Dark mode will reduce screen brightness by 40-60% compared to light mode”

Chain of Thought Prompting 

With chain of thought prompting, you break complex tasks into sequential, logical steps which guide the AI through a reasoning process. This is especially useful for product strategy or feature prioritization decisions.

For example, rather than asking "What features should we build next quarter?" after adding in all your data, try the following.

Prompt Example:

"Let's think step by step about our Q3 product roadmap prioritization: 

1. First, analyze our current user retention metrics and identify the key drop-off points in the customer journey.

2. Next, evaluate which user personas are most affected by these friction points.

3. Then, identify potential solutions that would address these specific pain points.

4. For each potential solution, estimate development effort and potential impact on our north star metric.

 5. Finally, recommend a prioritized feature list with implementation timeline and success metrics.

I’ll start by uploading the information relevant to each journey step…”

This approach ensures the AI considers all relevant product factors rather than jumping straight to feature suggestions, resulting in more strategic and well-reasoned product recommendations.

Understanding how to leverage both few-shot and chain of thought prompting can level up your experience and outputs leveraging AI. 

Evaluating AI Outputs: Trust, Verify, and Consider Ethics

As product managers integrate AI into their workflows, balancing the efficiency AI provides with proper evaluation and ethical considerations becomes crucial.

Here's how to approach AI outputs thoughtfully.

Critical Evaluation: When to Trust and When to Verify

Always approach AI-generated content with informed skepticism. While AI can produce impressive results, it's trained on historical data and may not account for your specific context or latest developments. In fact, as of March 2025, ChatGPT’s knowledge cut-off date is June 2024 (which can be found out by asking the model). 

Here are a few red flags to watch for:

• Specific metrics or statistics (check the sources)
• Definitive market predictions without adequate supporting evidence
• Overly simplified solutions for complex problems
• Information contradicting known industry standards

Addressing Bias and Ethical Implications

Because AI models are trained on historical data, they may contain embedded biases that don't reflect your aspirations for equitable products:

• Question representativeness: Ask "Whose perspectives might be missing from this analysis?" and "Does this work for all our user segments?"
• Test recommendations across diverse scenarios to ensure they work equitably
• Supplement AI insights with direct feedback from underrepresented user groups
• Regularly audit your AI workflows for potential bias reinforcement

Transparency and Responsibility

Be open with your team and stakeholders about how you're using AI in your product management process. Clearly distinguish between AI-generated content and human analysis. Explain your process for validating AI recommendations, and maintain accountability for decisions, even when AI-assisted. 

Finally, make sure to follow the established company guidelines for appropriate AI usage, especially regarding customer data and proprietary information.

AI as a Collaborative Partner

Effective AI prompting isn't about replacing human creativity—it's about augmenting it. A product manager’s unique value lies in strategic thinking, empathy, and nuanced decision-making. AI is a powerful tool that can accelerate and enhance these capabilities.

Start small, experiment continuously, and view AI as a collaborative partner in your product management journey.

Bonus Pro Tips:

• Use Productboard’s best AI prompts for product managers.
• Keep a "prompt library" of your most effective prompts and share them with your team.
• Regularly review and refine your prompting techniques.
• Maintain a critical and curious mindset.