ClassPulse app PRD: From anonymous feedback idea to shipped MVP
This page provides a real, filled-out PRD for the ClassPulse app—an anonymous classroom feedback and rating tool. You'll see a concrete PRD, a builder comparison (Lovable, Cursor, Claude Code, Replit), and a clear step-by-step on how to go from idea to working MVP in 3 weeks or less. Internal guides and prompt starter included.
What this is
The "ClassPulse app PRD" is a Product Requirements Document specifying a web or mobile application where students can anonymously submit feedback and ratings for each teacher's lesson. In product and engineering terms, the PRD breaks down objectives, key flows, user stories, data models, constraints, and measurable outcomes for teams using tools like Lovable (for rapid AI-backed UI and Supabase DB scaffolding) or Cursor (for high-fidelity spec-to-code handoff). This artifact ensures alignment among design, product, and engineering teams. Frameworks like Next.js or Replit can be used to deploy the MVP in as little as 3 weeks, reducing wasted cycles by up to 40%.
Compared to alternatives
| Option | Best for | Trade-off |
|---|---|---|
| Lovable | Building MVPs fast with Supabase, React, and AI-generated UIs. Great for web and mobile. | Less fine-grained control over custom logic or unique UI patterns. Rapid, but sometimes less flexible. |
| Cursor | Turning detailed PRDs directly into high-fidelity React or Next.js code with AI-powered dev assist. | Needs a tight PRD and manual review. Not ideal if you want plug-and-play DB or deploy. |
| Claude Code | Instantly brainstorming and generating production-grade backend logic, feedback data models, and AI-driven flows. | Can lose detail in complex multi-model interactions. Not plug-and-play with UI scaffolds. |
| Replit | End-to-end prototyping, testing, and deploying in one online IDE. Fastest way to ship full-stack MVPs solo. | Less enterprise-ready for scale, and early-stage code quality may need tightening for prod. |
A real example
Product Requirements Document: ClassPulse App
Objective
Build a mobile/web app to let students anonymously rate and review each classroom session. Teachers view aggregated feedback to improve lessons.
User Roles
- Student: Submit anonymous ratings (1-5) and written feedback per teacher per lesson.
- Teacher: Review feedback dashboard, see trends and anonymized quotes. Cannot identify individual students.
- Admin: Manage school, add teachers, moderate flagged content.
Core Features
- Anonymous Feedback Flow: Students select lesson, submit rating (1-5 stars) and optional comment. Submission is anonymized.
- Aggregated Dashboard for Teachers: Teachers see average scores per lesson, feedback trends over 30 days, and flagged issues. No single student's identity is visible.
- Flagged Reporting: Offensive comments automatically flagged using AI (Claude Code + Content Safety API), admin review required.
- Authentication & Access: Students log in via school email (Supabase Auth), but submissions are delinked from identity. Teachers/Admins have role-based access.
- Data Storage: Ratings and comments stored in Postgres (Supabase), anonymization layer strips PII before persist.
Success Metrics
- Launch MVP in 3 weeks.
- 85% student participation per week in pilot classrooms.
- <1% flagged abusive submissions
- 40% reduction in negative teaching feedback by month 2.
Non-Goals
- No live chat, attendance, or assignment tracking.
Constraints
- Anonymity must be unbreakable—no reverse-ID even for Admins.
- Daily data exports to secure S3 bucket (Vercel + AWS Lambda trigger).
- Responsive UI: mobile-first, works on low-end Chromebooks.
Sample Flow (Lovable/Next.js/Supabase)
- User lands on home (Next.js SSR, Vercel hosting)
- Auth via Supabase (email magic link)
- Student: Select today’s lesson ➔ Rate ➔ Submit (API call delinks identity)
- Teacher: Dashboard page shows summary: average rating, top comments, flagged feedback
- Admin: Review flagged content via Supabase Studio
Out of Scope
- No parent/guardian logins
- No gamification or badges
Tech Stack
- Frontend: Next.js, Vercel
- Backend/DB: Supabase, optional Claude Code moderation
- Builder fit: Lovable for UI scaffolding
- AI: Claude Code for flagging
How to use this
- Define user roles and core flows: List out the student, teacher, and admin roles. Draft user stories for each, emphasizing anonymity for feedback. Specify submission, review, and moderation flows.
- Write out detailed feature list and non-goals: Translate user flows into requirements: anonymous feedback, dashboard, flagged content review, and authentication. Explicitly note what won't be built.
- Choose your AI builder and tech stack: Decide between Lovable, Cursor, Claude Code, or Replit based on your need for speed (Lovable), spec-to-code (Cursor), or solo dev (Replit). Supabase for auth and DB; Next.js for frontend.
- Draft the first PRD iteration: Use the filled example above or adapt a related open template from our PRD template hub. Nail down features and constraints. Get feedback from at least one engineer and teacher.
- Map to builder requirements: Format your PRD/report for your chosen builder—Lovable likes explicit user flows and DB tables; Cursor performs best with UI sketches linked to flows; Replit prefers clear endpoints and data model.
- Kick off AI build and review outputs: Generate your initial version. Manually review for anonymity enforcement, data security, and that all core features are accounted for. Run a quick usability test—can a student submit feedback in under 5 taps?
FAQ
What’s the best builder for a ClassPulse MVP?
If you want to launch in 2–3 weeks with a ready-to-go UI and authentication, Lovable (Supabase + React) is fastest. Cursor is great if you have detailed specs and want pixel-level React output. If you prefer hands-on code or want to iterate live, Replit is ideal.
Is anonymous feedback really unbreakable?
If you use Supabase Auth but immediately delink submissions from user IDs, it’s very hard to reverse-engineer. Don’t store PII alongside feedback. Always audit the architecture—admin should never be able to cross-reference logs with submissions.
How do you handle abusive feedback?
Use content moderation AI like Claude Code or OpenAI’s Moderation endpoint. Automatically flag abusive or inappropriate content and route to admin review. Keep flagged data isolated from general teacher dashboards to protect privacy.
Can this be extended to supporting video feedback or audio?
Yes, but video/audio require more complex storage and potentially moderation upgrades. Start with text; if feedback volume and storage are manageable, pilot audio or video for a subset of users and monitor engagement before scaling.