Best Practices for Using PRDs with Cursor

Introduction to Cursor & AI-Assisted Development

AI-assisted development is transforming how product teams build software. Cursor is a prime example – it's basically everyone’s favorite AI code editor​.

Cursor is a fork of VS Code enhanced with AI capabilities. It integrates large language models (LLM) to provide smart code completions, refactor using suggestions, and provide in-editor AI chat for guidance. This means Cursor can look at your entire project context and assist in ways a standard code editor cannot, such as suggesting multi-line code completions and auto-importing needed libraries based on usage​.

In this new coding workflow, Product Requirement Documents (PRDs) play a critical role. A PRD is a document that outlines what the product should do – features, functionality, constraints, and so on – essentially a blueprint for development.

In AI-assisted coding, a clear PRD becomes more than just a team reference; it's also a guide for the AI. By feeding Cursor a well-structured PRD (or having it accessible in the project), you give the AI a source of truth about your product's goals.

In short, a good PRD helps both humans and the AI stay aligned on what needs to be built.

Structuring PRDs for Cursor

Not all documentation is equally digestible by AI. To use PRDs effectively with Cursor, you should structure them in an AI-friendly way. The goal is to break down requirements into clear, consumable chunks​ that Cursor’s LLM can easily interpret. 

Here are some best practices for structuring your PRD:

• Use Clear Sections: Organize the PRD with headings for each major section (e.g. Introduction, Problem Statement, Solution/Feature Overview, User Stories, Technical Requirements, Acceptance Criteria, Constraints). A consistent format helps the AI locate relevant information quickly. Need ideas for what should go into your PRD? Check out our PRD templates.
• Write Sharp User Stories: Under a features section, write user stories or feature descriptions in concise language. It’s fine to use the classic format (e.g. “As a user type, I want some capability so that benefit.”), but make sure each story is isolated and clearly stated. This lets Cursor pick up on specific feature needs when generating code. Avoid burying multiple requirements in one long paragraph – split them out instead.
• Explicit Acceptance Criteria: List acceptance criteria as bullet points for each feature or user story. These criteria define done-definition and edge cases. For example: “Users can reset their password via email link,” “The link expires after 15 minutes,” “An error message is shown for invalid or expired links.” Writing them as a bulleted list ensures the AI sees each condition distinctly. Cursor will use this clarity to guide code suggestions that meet those conditions.
• State Constraints and Non-negotiables: If there are specific constraints (technical or business), call them out in their own section or subsection. For example: “Constraints: Must use OAuth 2.0 for authentication; support at least 10,000 concurrent users; no external cloud services due to compliance.” By highlighting these, you ensure the AI doesn’t propose solutions that violate them. Clearly labeled constraints make it easier for Cursor’s AI to adhere to required technologies or architectural decisions.
• Include Technical Specs & Business Logic: Provide any known technical guidelines: architecture outlines, data models, or algorithms. If your PRD includes a Technical Specifications section, use it to mention frameworks, APIs, data flow diagrams, etc. The AI can leverage this info. For example, if the PRD notes “Feature X will use a state machine to manage workflow”, Cursor can factor that into its suggestions. Similarly, describing critical business logic (rules or formulas the software must implement) in plain language will help the AI generate code that aligns with those rules.
• Use Consistent Formatting: Maintain a consistent format for similar items. If you list user stories as User Story 1, User Story 2, do the same throughout. If acceptance criteria are bullet points starting with a verb, keep that style. Consistency helps the AI parse the document structure. Also, use simple language and define acronyms (at least once) so the AI (and any new team members) understand them. Avoid ambiguous wording; AI can misinterpret vagueness, just like humans.

By structuring your PRD with clear sections, bullet points, and straightforward language, you essentially make it AI-ready. Cursor’s workflow benefits from this because the PRD can be indexed and referenced easily by the AI, resulting in more accurate code generation. In summary: think of writing the PRD not just for your engineers, but also for the AI assistant – clarity and organization are key.

Using .cursorrules to Maintain Project Consistency

Cursor provides a unique feature called Cursor Rules (configured via a special file, traditionally named .cursorrules). This is a powerful way to enforce project-wide conventions and guide the AI’s coding style. A .cursorrules file is essentially a set of custom instructions for Cursor’s AI to follow when generating code​. Think of it as a way to encode your team’s coding standards and best practices so that the AI automatically adheres to them.

What are Cursor rules? In essence, they act like a persistent system prompt or guidebook for the AI within your project​. When Cursor’s AI is about to generate or suggest code, it checks these rules first. This means you can dictate things like which frameworks to use, coding style guidelines, or any project-specific patterns. For example, you might tell the AI via .cursorrules that “we’re using React and TypeScript,” “always follow our ESLint style guide,” or “prefer functional components over class components in React.” All these can be set as rules, so the AI will obey them in its suggestions​.

How to set rules: In older versions of Cursor, you’d create a single file named .cursorrules in the root of your repo and list all instructions there. For instance, a .cursorrules file might contain global directives like:

# Project Cursor Rules

- Use Python 3.10 and Django REST Framework for backend APIs.

- Follow PEP 8 style guidelines for all Python code (linting enforced).

- Use React 18 with TypeScript for frontend; all components should be functional.

- Utilize Tailwind CSS for styling; do not use inline CSS styles.

- All database access must go through the ORM (no raw SQL queries).

When present, those rules would influence the AI’s output, ensuring consistency. If a developer prompts Cursor to create a new API endpoint, the AI will already know it should use Django and follow PEP 8, without the developer explicitly saying so each time.

Project-wide conventions: Product managers (PMs) and engineers can collaborate to encode conventions in .cursorrules. This might include naming conventions, architectural patterns, or dependency choices. For example, if your team has decided on a specific logging library or an error handling pattern, adding a rule for it means the AI will incorporate that by default. This keeps the whole team aligned. It’s especially useful in larger teams – with a shared rules file, everyone gets consistent AI suggestions, promoting a uniform style of code across the project​

Examples of effective rules: To illustrate, here are a few rule lines one might include:

• “Always use the axios library for HTTP requests (do not use fetch).”
• “In React components, prefer using Hooks (useState, useEffect) instead of class lifecycle methods.”
• “All strings shown to users must be internationalized (use i18n functions).”
• “Database interactions should go through the repository layer; do not query the ORM in view functions.”

By encoding such points, you ensure the AI’s suggestions respect these decisions. Without rules, the AI might suggest a mix of patterns (some suggestions using fetch, some using axios, etc.), which can lead to inconsistency or extra review work.

Maintaining the rules: As your project evolves, keep the rules up to date. You might start with a simple .cursorrules file, but as Cursor has evolved (version 0.45+), there's now support for organizing multiple rule files in a .cursor/rules/ directory (with each file targeting certain file types or scopes)​.

The idea is similar, but more granular – e.g., you could have one rules file just for frontend conventions, and another for backend. Whether you use one file or many, make sure to revisit them when your tech stack changes or you encounter new AI outputs that need guidance.

Note: At the time of writing, the single .cursorrules file is still supported for backward compatibility​, but it’s marked for eventual deprecation in favor of the new system. The best practice is to start using the new format (an instructions.mdc or similar Markdown-based rules in the dedicated folder) for flexibility. But if you stick with one file, be cautious not to overload it (more on this in Common Mistakes below).

In summary, use .cursorrules to steer Cursor’s AI. It’s like having a project README that the AI always reads before writing code. When used well, it ensures the AI’s contributions are consistent with your architecture and coding standards, saving you time in code reviews and rework.

Leveraging Cursor’s Context Awareness

One of Cursor’s strongest advantages is its context awareness. The editor doesn’t operate on a single file in isolation – it can take into account your entire project and even the conversations you have with it. To get the most out of this, you should strategically provide context to Cursor and understand how it uses that context.

Codebase indexing: Cursor automatically indexes your project files to build a semantic understanding of your codebase​. In practice, this means the AI can refer to other files when needed.

For example, if you’re editing a file and use Cursor’s code search or ask a question, it can retrieve relevant info from elsewhere in the project. This context-aware behavior is evident in features like smart auto-imports – when Cursor suggests a piece of code that uses a symbol from another module, it will also suggest adding the appropriate import statement by looking at the project structure​. It even tries to anticipate what you'll do next, using knowledge of your project structure to help you scaffold code.

As a user, you don’t have to manually copy-paste content from one file to another; Cursor’s AI already has a birds-eye view (within the limits of its context window).

Providing the right documentation: To leverage this, include your PRD and relevant documentation in the project. For instance, you might add the PRD as a Markdown file in your repository (e.g., docs/PRD.md or even a simple README.md with the requirements). Because Cursor indexes all files by default​, the content of the PRD becomes queryable context.

If you ask in Cursor’s chat, “What are the requirements for feature X?” or start implementing feature X, the AI can refer back to that PRD file to recall details. This is a huge win – it’s like having the spec constantly on hand for the AI. Tip: Keep the documentation updated in the repo; if something changes in requirements, update the PRD file so the AI doesn’t work off outdated info.

Context in prompts: When using Cursor’s chat or “Agent”, you can explicitly reference context. For example, you might say: “Refer to the user stories in PRD.md and generate the data model classes for those requirements.” Cursor will then pull in the content of PRD.md (or the relevant parts) to craft its answer. You can also open the PRD file side-by-side and manually copy snippets into your query if needed. The key is to make sure the AI has access to the information. If something is important (like an architectural diagram or an API contract), ensure it’s either described in the PRD or another doc in the codebase, or directly feed it into the conversation.

Architectural context: Provide high-level architecture details to Cursor so it understands the big picture. This could be done via the PRD (e.g., a section describing system architecture), in code comments, or via .cursorrules.

For instance, if the PRD specifies a microservice architecture with certain boundaries, mention that. Then when you ask Cursor to generate code for a component, it will be mindful of those boundaries (e.g., not calling another service’s database directly). If you have an architecture.md file, consider referencing it in a Cursor rule or just keep it in the repository for the AI to find.

Interactive refinement: As you develop, you can iteratively refine AI-generated code by leveraging context. A common pattern is:

1. Prompt Cursor to generate an initial solution (e.g., “Implement the login feature as described in the PRD”).
2. Review the output against the PRD’s acceptance criteria. If something is missing or off, point it out in a follow-up prompt. For example: “The PRD says the password should be at least 12 characters. Update the implementation to enforce that.” Because the AI has the PRD context, it will recognize that requirement and adjust the code.
3. Use in-editor cues: You can highlight a block of code and ask Cursor’s chat “Explain how this meets the acceptance criteria” or “Optimize this according to our performance constraints.” Since those criteria and constraints are documented, the AI can double-check against them.

Leverage conversation memory: Cursor’s chat will remember the current conversation (up to a limit). This means you can have a back-and-forth: “Generate function X.” -> “Now add error handling as per requirements.” -> “Great, now write tests for that following the acceptance criteria.” Throughout this, the PRD and any .cursorrules are silently guiding the AI. It’s context-aware enough to incorporate those details without you always restating them.

Caution with context size: Note that while Cursor is context-aware, there is a limit to how much it can hold in working memory at once (determined by the AI model's token limit). Very large PRDs or codebases might exceed this. Cursor’s indexing helps by smartly fetching only relevant chunks, but if you have a huge PRD, consider splitting it or focusing the AI on relevant sections (you can tell it which section to look at).

Also, use .cursorignore if there are large files the AI should skip​ (e.g., big data files or libraries) to free up more focus on the important docs.

In short, make context work for you. Put the PRD and specs where Cursor can see them, reference them in your prompts, and let the AI draw on the whole project knowledge. This way, the code Cursor writes will be more aligned with your existing code and the documented requirements, reducing miscommunication between what the PM envisions and what gets implemented in code.

Step-by-Step Examples & Use Cases

To ground these concepts, let's walk through an example workflow using Cursor with a PRD, and show snippets of what the PRD and .cursorrules might look like. We’ll use a hypothetical feature and demonstrate how a product manager and engineering team might collaborate using Cursor.

Example Scenario: Build a “Task Manager” feature where users can create to-do items, mark them complete, and categorize them.

Writing an AI-Friendly PRD

The product manager (or tech lead) starts by writing a PRD in Markdown, saved as docs/task-manager-prd.md in the repo. It might look like this:

# Task Manager Feature PRD

## Introduction

This feature allows users to create and manage their to-do tasks within the app. It should be intuitive and fast, helping users keep track of their work.

## User Stories

- **US1:** *As a user, I want to add a new task with a title and due date, so I can remember to complete it on time.*

- **US2:** *As a user, I want to mark a task as completed, so I can track what I've done.*

- **US3:** *As a user, I want to categorize tasks by project, so I can view tasks by context.*

## Acceptance Criteria

**For US1 (Add Task):**

- The user can open a “New Task” form and input a title (required) and an optional due date.

- If the title is empty, the app shows a validation error and does not save.

- Upon save, the task appears in the task list with the provided title and due date.

- The system automatically sets a creation timestamp for the task.

**For US2 (Complete Task):**

- Each task in the list has a “Mark Complete” option (e.g., a checkbox or button).

- When marking complete, the task is visually distinguished (e.g., crossed out or moved to a ‘Completed’ section).

- Completed tasks’ data is persisted (they should remain marked complete on page refresh).

- There is an option to undo a completion (mark it active again).

**For US3 (Categorize Tasks):**

- Users can assign a task to a project category (selected from a dropdown list of project names).

- The “New Task” form allows selecting a category. This is optional; tasks with no category go into “General”.

- On the main UI, users can filter the task list by category (e.g., view only tasks for a selected project).

- If a project category is deleted from the system, tasks under that category should default to “General”.

## Constraints

- Must use the existing front-end stack: React (TypeScript) with our in-house UI components library.

- Use Redux for state management (since the app already uses Redux).

- Follow accessibility guidelines (all features should be usable via keyboard and screen readers).

## Technical Notes

- Leverage the existing `Task` model in the backend API (it has fields: id, title, dueDate, completed, categoryId, createdAt).

- API endpoints already exist for creating tasks and updating task status. Use the `POST /tasks` and `PUT /tasks/{id}` endpoints.

- The front-end should utilize our API client module for all requests.

This PRD is structured with clear sections and uses bullet points for criteria, making it easy for the AI (and humans) to parse. Now the team has given Cursor a comprehensive blueprint. The PRD lives in the repo, so Cursor will index it and can pull details from it as needed.

Creating a .cursorrules File

Next, the engineering team sets up project rules to ensure Cursor’s output follows their conventions. They create a file .cursorrules (for simplicity in this example) at the root of the repository:

# Cursor Rules for Task Manager Project

- Use **TypeScript** for all new frontend code.

- Follow the **Airbnb JavaScript style guide** (our linter is configured for this).

- All React components must use **functional components and hooks** (no class components).

- Use our internal UI library components (from `@ourcompany/ui`) instead of raw HTML elements when available (e.g., use `<Button>` from the library).

- State management: use **Redux** and our existing slices (avoid using React local state for tasks).

- API calls: use the `apiClient` provided (`import { apiClient } from '@/utils/apiClient';`) rather than `fetch` directly.

- **Testing:** 

For each new module, include a simple Jest test file skeleton.

These rules cover a lot of ground. They ensure that when Cursor suggests code, it will do things like use TypeScript syntax, use the team’s UI components, and maybe even stub out a test. The rule about Redux will, for instance, nudge the AI to update the Redux store or use dispatch actions when appropriate instead of something inconsistent. Essentially, this .cursorrules file encodes the team’s preferences so the AI’s contributions fit in seamlessly.

(In new versions of Cursor, the team could split these into multiple files under .cursor/rules/ – e.g., a frontend.mdc for frontend-specific rules and a testing.mdc for testing conventions – but the content would be similar.)

Leveraging Context to Build the Feature

Now it’s time to develop. An engineer opens Cursor, which loads the codebase. Thanks to codebase indexing, Cursor’s AI is aware of the existing code (like models and API client) and also has the PRD and rules in context. The development might go like this:

• The engineer creates a new React component file TaskList.tsx and types a high-level comment or prompt: “// Component to display list of tasks with options to add, complete, and filter by category”. Cursor, seeing this and armed with the PRD, might suggest an outline of a component structure. It could even incorporate bits of functionality (like a button labeled “New Task” or a filter dropdown) because the PRD mentioned those features. Because of .cursorrules, the suggestion will be in TypeScript and using the correct library components (e.g., maybe it suggests <Button> from the UI library for “New Task”).
• The engineer then asks Cursor via the chat: “Create a form component for adding a new task (title, due date, category) as per the PRD.” Cursor will reference the PRD’s "Add Task" acceptance criteria and generate a NewTaskForm.tsx component. This might include form fields for title, due date, category dropdown, and validation logic (e.g., preventing empty title submission) since those were explicitly in the PRD. The .cursorrules will ensure it uses, say, the proper form components or follows the coding style. The engineer reviews the generated code, sees that the required validations are there, and maybe just tweaks text or slight details.
• Next, for marking tasks complete, the engineer might write in the existing TaskItem component: // TODO: implement handleCompleteTask and then ask Cursor to fill that in. Cursor refers to US2 in the PRD and realizes that marking complete should update some state and call the API. It writes a function that dispatches a Redux action like completeTask(taskId) and calls the PUT /tasks/{id} endpoint via the apiClient.
  All of this aligns with the PRD and technical notes. It might also include a comment “// TODO: handle undo if needed” because the acceptance criteria mentioned an undo option – reminding the dev to implement that (or even implementing it if the context was clear enough).
• When implementing filtering by category (US3), the developer can use Cursor’s search to recall how categories are structured. For example, they might ask: “What is the type of category in our system?” If there's a model or type defined elsewhere, Cursor will find it. Suppose there’s an existing Project model or a constant list of categories; Cursor fetches that info due to indexing. Then the engineer adds a dropdown for categories. Cursor helps by suggesting code to filter the tasks list when a category is selected. It might produce something like a Redux selector or a React state filter, again following the PRD's description that tasks with no category are "General", etc.
• Throughout this, the engineer and Cursor are effectively pair-programming. If the AI’s suggestion misses something from the PRD, the engineer can explicitly mention it: “Include the timestamp setting when adding a task (as per requirements).” The AI will then adjust the code (perhaps ensuring that after creating a task via API, it displays the createdAt timestamp or sets it in state).
• Thanks to the .cursorrules, all code so far matches the team’s standards. For instance, any API call suggestions used apiClient (per rules). If the AI accidentally suggested using fetch, the engineer would notice that conflict with .cursorrules and could correct it, but ideally the rule prevented that suggestion in the first place.

Example Outcome

At the end, the team has a new feature built quickly. The PRD served as a roadmap for both the human and the AI. Cursor’s suggestions saved time by writing boilerplate and ensuring edge cases (like validation) weren't forgotten (since they were in the PRD, the AI included them). The .cursorrules kept the style consistent – if another engineer on the team opens the same project and uses Cursor, they’ll get similar suggestions aligning with what’s already been done.

Testing and Refinement

Suppose one acceptance criterion was missed (maybe “show an error if due date is in the past” which wasn’t in the PRD initially). The team can update the PRD to add this criterion, and then ask Cursor to update the code: “According to PRD, add validation to prevent adding tasks with past due dates.” Cursor will comply, adding that logic. This highlights an iterative use: keep PRD and code in sync, and use the AI to propagate changes as requirements evolve.

Real-World Use Case

Many Cursor users follow a similar pattern. They start by writing a detailed PRD or spec, often with sections for purpose, features, and requirements. Then they create a .cursorrules to set the ground rules for code style.

Finally, they implement feature by feature with the AI. The AI essentially reads the PRD and translates it into code under the programmer’s supervision.

This approach can significantly speed up development while reducing the chance of misimplementing a requirement. It’s especially useful for solo developers or small teams – the PRD acts as the “second brain” for the AI, guiding it, while .cursorrules acts as the safety rails.

Common Mistakes & How to Avoid Them

While using Cursor with PRDs and custom rules is powerful, there are some common pitfalls. Being aware of these can help you avoid frustration and ensure you get the best results from the AI.

Mistake 1: Vague or Overly Broad PRDs

If your PRD is too high-level or ambiguous, Cursor might generate generic or incorrect code. For example, if a requirement says “The system should be secure” without specifics, the AI can’t infer what to do (encryption? input validation? something else?).

Avoid It: Make requirements actionable. Include specific acceptance criteria or examples. If something must meet a standard (e.g., “follow OWASP security guidelines”), list key points of it relevant to your project. Essentially, be specific in the PRD so the AI isn’t left guessing. If you notice the AI output is off-target, check if the PRD wording might be interpreted differently than you intended and clarify it.

Mistake 2: PRD Not Accessible to Cursor

Sometimes users forget that the AI needs the PRD text accessible. If you keep the PRD in a separate document that Cursor hasn’t indexed (or is not provided to it), it obviously won’t magically know those requirements.

Avoid It: Always have the PRD content available to Cursor – put it in the repository or paste key parts into the chat when prompting. Ensure it’s not excluded by a .cursorignore file by accident. A good practice is to keep the PRD in a docs/ folder in the project (or even in the README) so it's always in context.

Mistake 3: Dumping Huge PRDs Without Structure

On the flip side, giving Cursor a 30-page unstructured PRD and expecting perfect adherence is unrealistic. The AI has a limit to how much it can consider at once. If the PRD is monolithic, the AI might miss details or get confused about priorities.

Avoid It: Break down large PRDs. You can have multiple docs (feature-specific PRDs) or at least break the conversation into sections: tell Cursor to focus on one feature at a time. Use headings and keywords in the PRD so that even if only parts of it are fetched as context, they still make sense. You can also manually feed sections of the PRD when working on that part of the project. Think modular – align the breakdown of the PRD with how you'd implement in stages.

Mistake 4: Overloading .cursorrules

It’s possible to have too many rules. If you stuff every minor detail into one giant rules file, it could consume the AI’s context window and even lead to contradictory instructions. Earlier versions of Cursor that relied on a single .cursorrules file saw issues where the AI didn’t know which rules mattered and the prompt got cluttered​.

Avoid It: Keep rules focused and high-level. Don’t try to micro-manage the AI with dozens of very fine-grained rules (like 100 specific naming conventions). Use rules for broad strokes and critical patterns. And take advantage of scoping – e.g., use separate rule files or scoped rules (if using the new system) for different parts of the project, so the AI only loads what's relevant. This way, the AI’s context isn’t filled with irrelevant rules when you’re working on a certain file.

Mistake 5: Conflicting Instructions

If your PRD says one thing and .cursorrules says another, the AI might produce inconsistent results. For instance, the PRD example might show pseudo-code using a library X, but .cursorrules says “don’t use library X, use Y”. Or two rules conflict (one rule says use semicolons, another says no semicolons).

Avoid It: Ensure consistency between your sources of truth. Update the PRD if decisions change, and likewise update the rules. If something in the PRD is outdated (e.g., originally planning MySQL but later switched to Postgres), either change the PRD or inform the AI directly – otherwise it might suggest MySQL usage from that outdated info. It’s generally best if the PRD and the rules complement each other: PRD describes what to build; rules describe how to code it.

Mistake 6: Relying on the AI Without Verification

Cursor can generate code that compiles and even runs, but that doesn’t mean it’s 100% correct for your needs. A common pitfall is taking AI output at face value without testing or reviewing against the PRD. Maybe an acceptance criterion was subtly missed, or an edge-case isn’t handled.

Avoid It: Always review AI-generated code. Treat Cursor’s output as a draft that needs your approval. Write unit tests or at least manually test the feature to ensure it meets the PRD. The AI can help write those tests too – you can ask Cursor to generate test cases for each acceptance criterion. This not only checks the code but can reveal if the AI misunderstood something (if it writes a test you didn’t expect, that might highlight a misinterpretation).

Mistake 7: Not Updating Rules/PRD as You Iterate

Software projects aren’t static. If you learn something new or change a requirement but don’t update the PRD or .cursorrules, you might find the AI’s later suggestions are misaligned.

Avoid It: Keep documentation in sync with reality. If during development the team decides to use a different library or approach, update the rules file so the AI doesn’t continue suggesting the old way. Likewise, mark changes in the PRD so anyone (or the AI) referencing it is on the same page. Think of Cursor as a team member who reads these docs – it won’t know unless you tell it.

By being mindful of these pitfalls, you can correct course early. For example, if you notice Cursor giving a weird suggestion, it’s often either due to ambiguous input (so clarify your prompt or PRD) or conflicting context (so check your rules and docs). A bit of upfront effort in writing a good PRD and setting sane .cursorrules pays off immensely by preventing these issues.

Final Recommendations

Bringing it all together, here’s a summary of best practices for integrating Cursor and PRDs into your development workflow:

• Start with a Solid PRD: Before coding, ensure you have a well-thought-out Product Requirements Document. Include the why and what of the feature in clear terms – user stories, acceptance criteria, constraints, and any technical guidelines. This document will guide both your team and the AI. The clearer it is, the better code you’ll get.
• Structure for AI Consumption: Write the PRD in a structured format (Markdown works great). Use headings, bullet points, and concise language. Break requirements into smaller chunks or sections. Essentially, format your PRD like you expect an engineer or an AI to use it as a checklist.
• Use .cursorrules to Set the Ground Rules: Leverage Cursor’s project rules to enforce consistency. Define your stack and conventions (language, frameworks, style guides, etc.) in the rules file(s). This ensures the AI suggestions are tailor-made for your project, following the same standards as your human developers. It’s a one-time effort that will keep paying back with each code suggestion aligning to your guidelines.
• Keep Documentation in the Loop: Have your PRD and any relevant docs (API specs, data models, architecture notes) accessible to Cursor – ideally version-controlled in the repo. Cursor’s context awareness means it can pull information from these sources when needed. The more context it has, the more accurate and on-spec its code will be. Don’t hesitate to remind the AI about a specific part of the PRD if you are focusing on that – e.g., “According to the requirements (section X), do Y.”
• Iterate with the AI, Don’t Expect One-Shot Perfection: Use Cursor in a loop of code generation -> review -> refinement. Implement one user story at a time, or one module at a time, and compare the output against the PRD. If something is missing, ask Cursor to fix it. This incremental approach ensures each part meets the requirements before moving on. It’s much easier to fix issues at the story level than after the whole feature is done.
• Combine Tools Thoughtfully: If you also experiment with tools like v0.dev or Bolt.new for initial scaffolding, consider importing that scaffold into Cursor for fleshing out details. Cursor will help integrate the output of those tools with your specific requirements and style. On the other hand, if you mainly use Cursor + PRD from scratch, you likely won’t need those other tools. Choose what fits your workflow, but many teams find value in using the PRD-driven development with Cursor as the core, possibly with other AI tools as auxiliary for specific tasks (like UI generation or documentation).
• Validate Constantly: Even with a great PRD and Cursor’s help, always validate the final product. Ensure all acceptance criteria are met – you can use Cursor to generate unit tests or use cases to double-check each criterion. Run your test suite, do code reviews (the AI can even explain the code to you if needed). Treat Cursor as an assistant that greatly accelerates your work, but you are still the lead developer who verifies the quality.
• Learn and Tweak: As you use Cursor, observe how it responds to your PRD and rules. You might find ways to phrase requirements that yield better code. For instance, if you notice the AI ignores a certain paragraph, maybe turn that into a bulleted list or a bold note. Over time, you’ll get a feel for prompting Cursor effectively (some even call this “prompt engineering”). Don’t be afraid to adjust your PRD format or rules as you learn what the AI pays attention to. The ecosystem is new, so continuous improvement is part of the process.

By following these best practices, product managers and engineers can create a synergistic workflow with Cursor. The PM provides clarity of what needs to be built (in the PRD), the engineering team sets the stage for how (in the rules and technical design), and Cursor’s AI accelerates the execution, bridging the gap between specification and implementation. This not only speeds up development but also helps ensure that the final product closely matches the intended requirements with fewer misunderstandings.

In conclusion, Cursor + a good PRD + solid rules = a powerful trio. You’ll spend less time writing boilerplate or explaining basic things to new developers, because Cursor will handle a chunk of that. Your team can focus on creative problem-solving and fine-tuning, while the AI assists with the heavy lifting of code generation.