How to Write a PRD for Vibe Coding Tools

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In the era of autonomous coding agents (e.g. Replit’s Agent 3), a well-structured Product Requirements Document (PRD) is crucial. A thorough PRD provides the AI with clear goals and context – without it, “no AI in the galaxy will be able to help you.” Taking time to refine your vision and requirements upfront dramatically reduces errors and “debugging” later. Below is an effective high-level PRD template and a recommended workflow (circa September 2025) to maximize success with AI coding tools.

What are the Key Sections of an Effective PRD for Autonomous Coding?

Product Summary & Goals: What are we building and why? Provide a brief overview of the application and its core purpose. Include the problem statement, who it’s for, and the key outcome or benefit. For example, clearly describe the user’s problem, the affected customer persona, and the impact solving it will have. Also state the primary objectives or success metrics (e.g. “Improve customer engagement by 20%”). This gives the AI a well-defined goal to aim for.

User Persona(s) & Use Cases: Who will use this product and how? Identify the target user or customer persona and their needs. Describe specific use cases or user stories that illustrate how the user will interact with the app. For instance: “As a travel planner, I can input a destination and get a generated itinerary…”. By specifying the persona and context, you ensure the AI understands the end-user’s perspective and desired experience. Include any important user scenarios, workflows, or user flows the solution must support (e.g. login flow, main task flow) – this helps the AI map out the application logic.

Functional Requirements (Features & Stories): What should the app do? List all key features and requirements in clear, concrete terms. It’s often effective to write these as brief user stories or bullet points. For each major feature, describe its functionality and any relevant acceptance criteria. Group related features or steps if needed (you can even outline milestones or phases for iterative development). For example:

• User can create an account via email and password.

• User can post a new blog entry (title, body, tags) and see it listed on their profile.

Ensure each requirement is specific (avoiding vague language) so the AI knows exactly what to build. Tip: breaking big features into smaller tasks makes them easier for an AI agent to tackle.

Non-Functional Requirements: What qualities or constraints must be met? Specify any performance, scalability, security, or compliance requirements critical to your product. For example, “The app should handle 1000 concurrent users”, or “All data must be stored encrypted at rest.” If the app must meet certain standards (privacy laws, etc.) or run on specific devices/browsers, note those too. These constraints guide the AI’s implementation choices. Without explicit non-functional specs, the agent might default to basic settings, so call out anything essential (like response time limits, uptime requirements, or platform compatibility).

Technical Constraints & Preferences: Any predefined tech stack or integration needs? If you have preferences or limitations regarding the technology, programming language, frameworks, or APIs, include them here. Autonomous agents will choose a language/framework on their own if not told otherwise, so if you must use (or avoid) something, say so. Examples: “Use Python with a Flask backend”, or “App must integrate with Google Calendar API”. Likewise, mention if the project should build on an existing codebase or follow a specific architecture. Clarity in technical constraints prevents the AI from going down the wrong path (for instance, building a mobile app in React Native when you wanted a web app).

User Interface & Design Guidelines: How should it look and feel? For UI-based applications, guide the AI on the desired UX. This could include style guidelines (e.g. “Use a clean, modern design with our company color palette”), layout requirements, or specific screens. It’s very helpful to attach wireframes or mockups if available – Replit Agent allows you to upload images for reference. For example, you might create a quick mock UI (using a tool or even drawn by hand) and tell the agent: “Match this design for the homepage”. Providing visual context or detailed UI requirements ensures the AI’s front-end output aligns with your vision. (If your product has no UI – e.g. just an API or automation – you can omit this, but do specify the interaction format like CLI, API endpoints, etc.)

Scope Boundaries (In-Scope vs Out-of-Scope): What’s included now, and what isn’t? Clearly delineate the scope of this build. List any features or ideas that are explicitly out-of-scope for the current product or MVP. This prevents the AI from “overbuilding” or getting sidetracked by nonessential features. For example: “Out of scope: social media login, since initial version will use email login only.” A well-bounded scope keeps the agent focused on “well-defined goals”, which is important for autonomous runs.

Acceptance Criteria & Testing Plan: How do we know it works? Define the conditions to consider each requirement fulfilled. This can be simple assertions or examples for each feature. (e.g. “When I submit the signup form with valid inputs, an account is created and I receive a welcome email.”) Given that Agent 3 can test its own work in a browser, providing explicit acceptance criteria or sample inputs/outputs helps it verify correctness. If you expect specific edge cases to be handled, mention them. Essentially, describe how you (or the AI) will test each function. This guides the AI’s built-in testing loop and ensures the final app meets the requirements.

Assumptions & Risks (optional but useful): List any assumptions made (about users, usage, dependencies) and known risks or unknowns. For example, an assumption could be “users have an internet connection” or “we will have access to API X for data.” Risks might be “API rate limiting could affect our app”. Stating these helps an AI understand the context and be cautious about certain areas. (While an agent won’t magically resolve business risks, it might, for instance, include checks or handle error conditions if it knows the assumptions.) This section is more for human planning, but it can indirectly keep the AI’s solutions realistic by clarifying what you’re assuming.

Each section above represents information the PM should fill out in plain language. Keep the writing clear and concise. Think of explaining the product to a very literal-minded junior developer – you want no ambiguity in what to build. Replit’s own guidance on prompting emphasizes being “clear, concise, and context-rich” with instructions. The good news: if you provide this level of detail, AI agents can truly shine. Users have reported that being thorough upfront – essentially writing a mini-PRD – is the “secret to speeding up your AI development.”

Workflow to Generate and Use the PRD (Sept 2025 Best Practices)

Writing a PRD from scratch can be time-consuming, but in 2025 you have AI to help! Here’s a recommended workflow that maximizes your chances of success:

1. Gather Inputs & Use a Template: Start by collecting all necessary information from stakeholders, just as you normally would. This includes user interviews, business goals, rough feature lists, etc. Then fill out a PRD template (like the sections above) with these details. Many PMs still begin this process manually, ensuring the core vision and constraints are captured in writing. Remember: It’s essentially “collecting and documenting stakeholder and functional requirements” – the quality of the PRD directly limits what the AI can deliver.

2. Leverage AI as a Co-Pilot for the PRD: Once you have basic information down, you can use an AI assistant to refine and expand the PRD. For example, you might prompt a tool like ChatGPT or Claude with your draft notes and ask it to structure the PRD, fill in any gaps, or even ask you clarifying questions. In one PM’s approach, they gave an AI a custom instruction to ask up to 10 clarifying questions before drafting the PRD – this helped ensure the AI fully understood the requirements (and prevented hallucinations). AI writing tools tailored for product management have emerged as well. (Notably, “AI copilot for PMs” apps like ChatPRD can transform ideas into structured PRDs.) These tools can draft sections like the Problem statement, user stories, etc., based on your inputs.

   Pro Tip: Provide the AI with examples or a format if you have a preferred style. For instance, show an example of a well-written user story or a sample PRD section. This “few-shot” approach guides the AI to follow your desired format. Also, set criteria for the AI (e.g. “PRD should be under 2 pages”) to keep it focused. After the AI produces a draft, review it carefully – verify facts, adjust any off-target suggestions, and make sure it aligns with your vision. The AI draft is a starting point, but you, as the PM, are the final editor.

3. Finalize the PRD & Include Supplementary Assets: Complete any remaining details in the PRD. Ensure persona and use-case details are clearly stated (the PRD should specify who the user is – e.g. a busy professional, a student gamer, etc. – as you noted, the persona could be anyone, but it must be explicit in the doc). Attach or link any supporting materials the developer/AI might need: for instance, upload your Figma wireframes or hand-drawn sketches of the UI, provide sample data or API docs, etc. In Replit, you can even upload the PRD text file and images into the project workspace so the Agent can reference them. The goal is to give the AI full context – code, design, and requirements all in one place.

4. Choose Your Build Strategy – One-Shot vs Iterative: Now it’s time to use the PRD with the coding agent. There are two main strategies:

   • One-Shot Autonomy: Since Replit Agent 3 can run up to 200 minutes autonomously and handle complex tasks, you may simply feed it the entire PRD (e.g. paste the requirements or indicate the file) and ask it to build the whole application. If your PRD is comprehensive and the goal is well-defined, Agent 3’s Max Autonomy mode is ideal for tasks with well-defined goals. The agent will attempt to scaffold the project, code all features, and even test/fix issues along the way automatically. This can feel magical – users describe “having it deliver the app I wanted… is truly remarkable.” However, in practice, many have found that asking the AI to do everything in one go can sometimes lead to mistakes or the agent getting “lost” on complex projects.

   • Iterative Prompting: A safer, community-recommended approach is to use the PRD to break the development into smaller tasks or prompts, and feed them to the agent step by step. In fact, experienced Replit users advise “don’t ask it to add more than 1 new feature at a time.” For example, you (or another AI) can read the PRD and generate a task list or sequence of prompts: Setup project structure, Implement user login, Build homepage UI, Connect to database, etc. One Reddit user suggests: “Create a product requirements document, upload it. Get ChatGPT or another AI to create prompts for it based on the PRD.” Similarly, a Substack guide advises breaking the work into roughly 5–7 specific prompts derived from the PRD and running them in order (one at a time) through the coding agent. This method ensures the agent focuses on one well-defined piece at a time, which is easier to manage and debug. In practice, some found this dramatically improved reliability: “I tried to make it do everything from the start and got nothing but errors. Now I tell it what I want to build, then build each feature one by one.” The iterative workflow might take a bit longer upfront, but it “slows down to speed up” – by providing proper context and checkpoints, you avoid costly tangents and often reach a working app faster overall.

5. Interact and Refine: Whichever strategy you choose, remain in the loop. If you go one-shot, monitor the agent’s progress and be ready to step in if it veers off (Agent 3 provides a live browser preview and test summaries as it works). If you go iterative, review each feature once implemented and compare against the PRD acceptance criteria before moving to the next prompt. You can always clarify or correct the agent between steps. Think of it as an agile process: the PRD is your backlog, and you’re guiding the AI through it. Many tools in 2025 (Cursor, Replit, etc.) support saving progress and using checkpoints so you don’t lose work as you iterate.

6. Testing and Feedback: Take advantage of the agent’s ability to test itself. Because you wrote clear success criteria and perhaps provided example outputs, the AI’s integrated testing (in Agent 3’s case) will check if those criteria are met. Still, do your own testing as well – click through the deployed app or run the automation to see if it truly behaves as intended. If something is off, you can prompt the agent with a specific fix (referencing the requirement it missed). The PRD serves as the source of truth for what “correct” means, both for you and the AI. It’s not uncommon to discover missing details during testing; update the PRD if needed and have the agent address the gaps. This forms a feedback loop to polish the application.

7. Deployment and Next Steps: Once the app meets the PRD specs in testing, you can consider it “done” for the initial version. Document any new ideas or deferred features that arose and save them for the next PRD iteration. The beauty of using a PRD with AI is that you now have a clear record of what was built. If you plan enhancements, you can start a new cycle: update the PRD (version 2) and again either feed it in one go or break into tasks for the agent to implement.

What are some Additional AI Tools to use?

It’s worth noting the ecosystem around AI development: Several platforms help with the PRD-to-app workflow. For example, some PMs use AI design tools to generate UI mockups from the PRD description, then give those to the coding agent. Others use specialized AI-driven prototyping tools (like V0 or Lovable) to create a quick front-end and then have Replit Agent build the backend. The key is to use the PRD as a foundation – you can plug it into different services. In one Medium case study, a PM had an AI generate a PRD and then fed it into Replit and Bolt to get a working prototype. This kind of pipeline is becoming more common.

Maintain Clarity and Control

Throughout the process, maintain a structured approach. It’s tempting to treat an AI agent as a magic box where you can throw a paragraph of ideas and get a full app, but real-world experience shows planning and structure are still indispensable. Think of the PRD as the “communication bridge” between you and the autonomous coder. A good PRD forces you to clarify the product, and it constrains the AI to stay on track. Whenever issues arise, refer back to the PRD: was something underspecified or ambiguous? If so, improve it and try again. One experienced founder noted that turning “vibe coding” into a structured, reliable process with PRDs and task lists is what “makes vibe coding actually work” in practice.

What does a good PRD Template for vibe coding look like?

To summarize, here’s a fill-in-the-blank template you can use for your PRD:

Product Name: [What is the app called?]

Summary: [One or two sentences on what the app does and why it matters.]

Problem & Goal: [What user problem are we solving? Who has this problem, and what outcome do we aim for?]

User Persona: [Describe the target user (e.g. age, profession, needs).]

Use Cases: [List key scenarios of how the persona will use the app.]

Features/Requirements:

• [First major feature & its details]

• [Second feature]

• […etc for all core features]

Non-Functional Requirements: [Performance, security, or quality needs, if any.]

Tech Constraints/Stack: [Preferred languages/frameworks, integrations, or “must/avoid” tech choices.]

UX/UI Notes: [Any design guidelines or references. Link or attach wireframes if available.]

Out of Scope: [Anything explicitly NOT to include in this version.]

Acceptance Criteria: [Conditions to validate each feature – e.g. “User can X and Y happens.”]

Assumptions: [Important assumptions about users or environment.]

Risks/Concerns: [Any known risks or open questions.]

(Feel free to add sections if needed – e.g. “Milestones/Phases” if you plan to build in stages. For a very simple app, some sections can be brief, but don’t omit the essentials.)

By filling in such a template, you ensure all high-level aspects of the product are thought through. This document then becomes the blueprint for the AI agent.

Final Thoughts

In September 2025, the best practice for AI-led development is a blend of old-school product management and new AI capabilities. You still need a solid PRD – that hasn’t changed – but now you also have AI to help draft it and to build from it. Invest the time upfront to make your PRD clear, specific, and “AI-ready.” As one PM put it, putting in this effort “will remove the cold start problem” when you hand off to the AI. In other words, slowing down to provide thorough context actually speeds up the outcome.

Finally, don’t be afraid to iterate. Treat the PRD and the AI’s outputs as living things – refine the document, and loop back with the agent as needed. With a strong PRD and a structured workflow, an autonomous coding agent like Replit’s can often “build, test and fix your app” in minutes or hours, turning your requirements into a running application. Many early users have been astonished by this capability, noting that having a clear spec allowed them to get an app in one prompt that would have taken a team weeks to develop.

In summary: A PM should fill out a PRD’s high-level sections (vision, users, features, constraints, etc.) in detail, possibly with AI assistance, and then use that PRD to guide the autonomous coding agent – either in one go if the project is well-defined, or via an iterative series of smaller prompts. This approach.

Do you want me to also shorten the text into a cleaner, reference-free PRD guide (more like a polished PDF-ready version), or keep this long detailed format?