From Idea to Execution: Writing a PRD That Works - Part 1

Welcome, Reader!

Thank you for visiting. This article distills what I’ve learned from real-world experience—focused on practical advice that’s actually worked for me. If you think writing a Product Requirements Document (PRD) is easy, remember:

“Making something simple is hard. Simplicity is the result of a deep understanding.”

My goal isn’t to lecture or waste your time; instead, I want to leave you with a fresh, actionable perspective on PRDs. By the end of this article, I hope you see PRDs— and their power to drive clarity— in a new light.

Let’s dive in…

What is PRD?

A PRD is a simple guide that explains what a product or feature should do and why it matters. It covers the goals, key features, and how everything is expected to work. This document helps different teams—like developers, designers, testers, and business leaders—stay on the same page and build the right thing, the right way.

In short, PRD is a blueprint that defines what a product should do, outlining its purpose, features, functionality, and behaviour.

Next, I’ll walk you through each component of an effective PRD and explain how it serves different stakeholders—engineering, design, business, security, and more.

Keep in mind: there’s no one-size-fits-all formula. Product development is inherently dynamic, and your PRD should continuously reflect new learnings, changing requirements, and stakeholder feedback.

The key is to treat your PRD as a living document—regularly revisiting and refining it to ensure clear alignment, communication, and successful outcomes across the team.

Key Components of PRD

A perfect PRD contains below mentioned sections,
Topics covered here,

• Overview

• Problem Statement

• Hypothesis

• How did we arrive?

  • Quantitative analysis

  • Qualitative analysis

  • Market and Competitor analysis

  • MVP Analysis

• Assumptions

• Minimum Criteria for Success (MCS)

Topics covered in Part 2,

• User Stories

• Requirements

  • Functional

  • Non-Functional

  • Security / Compliance

  • Dependency

• Acceptance criteria

• Design/UX References

• Scope

• Out of Scope

• Mitigation plans

• Timeline

Overview

Think of the Overview as your quick elevator pitch for the PRD. Instead of just “summarising what the document is about,” this is your chance to hook the reader—especially leaders or busy folks who might only read this section!

Keep it simple, punchy, and straight to the point. Highlight what problem you’re solving, what you’re proposing, and why it matters. If you were chatting with someone in the hallway and had only 30seconds to explain the project, what would you say? That’s the energy you want here.

Bottom line: This section sets the tone for the whole PRD—make it count, but don’t overthink it. Just be clear, confident, and let your excitement for the idea show!

Problem Statement

This is where you spell out exactly what problem you’re solving—not just in theory, but with real numbers whenever possible. Don’t just write in broad strokes; dig in and use data to make your case.

Try to answer:

• What’s happening right now that isn’t working?

• Who is affected, and how big is the pain point?

• Can you tie it to actual metrics—lost revenue, customer complaints, time wasted, security incidents, or anything else measurable?

For example:

Instead of: “Users find onboarding confusing.”

Try: “x% of new users drop off before finishing onboarding”

Bringing in concrete stats doesn’t just make your problem statement stronger—it shows you’ve done your homework and helps anyone reading your PRD immediately understand why this is worth tackling.

Bottom line: Avoid vague theory. Be specific, be honest, and back up your claims with real evidence whenever you can.

Hypothesis

Hypothesis is a one-liner that acts a tool to align stakeholders with your decision. Below mentioned is my sample template,

We believe that <action> will result in <reaction> because <reason>.

Example

We believe that simplifying the onboarding flow by reducing the number of steps from 7 to 4 will result in increase the onboarding completion rate by 15% because users currently drop off due to the lengthy process.

Why This Matters

A clear hypothesis helps:

• Align stakeholders on what you’re testing

• Define success metrics upfront

• Focus development on the most impactful changes

• Set expectations realistically

How did we arrive?

This is your chance to explain the thinking and evidence that led you to the hypothesis. Instead of just jumping to what you believe, walk the reader through the data, feedback, or patterns you uncovered along the way.

Bottom line:Sharing the “how” behind your reasoning sets the stage for everyone to understand and buy into the decisions you’re making.

Quantitative analysis

This is where you get to roll up your sleeves and dig into the numbers. In this section, your goal is to analyze real data—user metrics, patterns, or trends—to back up your hypothesis with hard evidence.

Example:

Analysis of the last 90 days shows only 47% of users completed onboarding, compared to an industry average of 66%. Digging deeper, we found that users who interacted with our help widget were 35% more likely to finish onboarding, suggesting targeted assistance helps reduce friction.

Why this matters:

• Provides objective, data-driven support for your recommendations (not just opinions).

• Helps stakeholders see the scale and urgency of the problem.

• Makes your hypothesis more credible and your next steps easier to justify.

Bottom line:Don’t just tell a story, show it with numbers.

Qualitative Analysis

While quantitative analysis helps drive decisions through data and numbers, qualitative analysis offers a different and equally valuable perspective. As a Product Manager, it's essential to step into the customer’s shoes to truly understand their problems—this insight often guides more empathetic and effective decision-making.

Keep in mind that not all users experience a feature in the same way. For instance, the UX of an onboarding page might feel intuitive to someone in their early 20s but confusing to a user in their late 30s. It’s your responsibility as a PM to identify these patterns and bring them to light.

Example:

Interviews with new users showed that while younger participants found onboarding intuitive, older users described it as “confusing” and “too fast-paced,” with many unsure about next steps. Support chats echoed this, with older users requesting more guidance and clarity.

This is where qualitative analysis becomes critical. A successful PM stays approachable and consistently connected to users, actively listening to their pain points to uncover nuanced insights that quantitative data alone can't reveal.

Why it matters:

• Brings empathy into your decision-making—helps you build products for real people, not just profiles.

• Surfaces blind spots in user experience that numbers can miss.

• Encourages solutions that are inclusive and resonate across your whole user base.

• Builds stronger relationships with your customers and more trust within your team.

Bottom line:As a PM, your job isn’t just to crunch data—it’s to connect with your users and ensure their voices shape your product decisions.

Market and Competitor analysis

“Timing and location aren’t luck; they’re the quiet rewards of preparation.”

This section is where you show you’ve done the homework beyond just building a feature—you understand who the customer is, where the market is headed, and exactly why now is the moment for your solution.

Why it Matters:

• Launching at the right time and to the right audience can be the difference between breakout success and falling flat.

• Staying aware of competitors helps you avoid blind spots and find opportunities for differentiation.

• Understanding your market ensures you build not just what users say they want, but what the broader landscape actually needs (and will pay for).

Tips for a Strong Section:

• Use a table or chart for quick-read competitor comparisons

• Highlight gaps in the market that only you address

Bottom line:This is your chance to prove you’re not just building in a vacuum.

MVP Analysis

This section gives you an optional but powerful chance to share what happened if you’ve already built a Minimal Viable Product (MVP) or early prototype. If you have data, attach your MVP analysis docs and summarize the key results right here.

What to cover:

• MVP Outcomes: How did your MVP perform? Include real-world feedback, usage metrics, customer reactions, and any unexpected results.

• Success? Build on It: If your MVP hit its targets, use the learnings, validated features, and positive data to guide (and justify) which requirements become part of the main PRD. Call out what worked and why—this forms a strong foundation for the rest of your plan.

• Failure? Celebrate Learning: If the MVP didn’t deliver as hoped, be open about it! Highlight what you discovered—pain points, missing value, technical or UX flaws, or even misalignment with user needs. Spell out the lessons learned, how those learnings shift your priorities, and what you’ll do differently in the full product.

Why it matters:

• Demonstrates you’re using real evidence to guide product direction—not just building on assumptions.

• Shows you’re learning from both wins and misses.

• Establishes credibility and sets a transparent, iterative tone for the rest of your PRD.

Bottom line:If you’ve run an MVP, let the data and learnings guide your next steps.

Assumptions

This is where you outline what you’re taking as given—the things you believe to be true as you move toward your solution. Listing your key assumptions upfront doesn’t just increase transparency with stakeholders—it shows that you’ve thought about “unknowns” and are purposeful (not just guessing) about how you plan.

How to make your Assumptions section strong:

• List each key assumption: Spell out the market, technical, user, or process beliefs that drive your decisions.

• Explain your reasoning: Briefly state why you think each assumption is valid. Did user research, analytics, industry best practices, or past experience shape your view?

• Tie to impact: Where possible, connect assumptions to possible risks if they prove wrong—and how you’ll address that.

Why this matters:

• Makes your thinking visible—which builds trust and prevents surprise disagreements later.

• Flags which ideas need to be validated as the project progresses.

• Gives everyone a head start for risk planning and prioritizing early validation.

Bottom line:Being open about your assumptions—and your logic—gives your PRD more credibility and helps everyone get on the same page, right from the start.

Minimum Criteria for Success (MCS)

"Success isn’t just reaching the goal—it’s knowing what goal is worth reaching."

This section is absolutely key. Without clear success criteria, nobody knows what “winning” actually looks like—and that makes it tough to align teams, measure progress, or celebrate real achievements. MCS draws the line in the sand: “If we hit these targets, we know the product or feature delivered value.”

Bottom line:Set your MCS before you start building and be honest about whether your product or feature is truly working.

So far, we've covered how a Product Manager engages with leadership, marketing, and fellow PMs. In the next part, we'll focus on sections what a PM should communicate to designers, engineers, and QA teams.