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Featured Work

PRD.ai : Feature-Level Documentation Intelligence Engine

PRD Intelligence Engine is a lightweight AI-powered prototype that transforms rough product ideas into structured Product Requirement Documents in seconds.

Watch 2-Min Demo Video - 

Live Prototype  -

​​Lightweight client-side prototype demonstrating structured generation logic (no backend integration).

Problem

As a Product Manager, I’ve spent countless hours writing PRDs.

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On average, it can take 5–10 hours a week to draft, structure, refine, and align on a single feature document. The work is important but much of it is repetitive:

  • Rewriting similar executive summaries

  • Reframing known user problems

  • Structuring user stories

  • Formatting functional requirements

  • Ensuring no critical section is missed

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When documentation isn’t structured well, the impact is immediate:

  • Engineers come back with clarification questions

  • Sprint planning gets delayed

  • Teams interpret scope differently

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PRDs are critical for alignment but the drafting process is largely manual and template-driven. That inefficiency felt like an opportunity.

Opportunity

Most PRDs follow a predictable structure.

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So I asked:

  • What if documentation sections were modular?

  • What if structured inputs could generate a strong first draft?

  • What if we could compress drafting time from hours to minutes?

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The hypothesis was simple:

A structured, AI-assisted drafting workflow can dramatically reduce documentation time while preserving clarity and rigor.

Solution

I built PRD.ai, a lightweight prototype that generates structured PRD drafts based on a few core inputs:

  • Feature name

  • Description

  • Target users

  • Primary goal

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Users can select which sections they want (Executive Summary, Problem Statement, User Stories, etc.), generate a draft instantly, and export it as Markdown.

This project focuses on workflow acceleration — not just AI novelty.

Important clarification:

This version simulates structured AI generation logic. A production version would integrate OpenAI API for real-time contextual drafting.

I intentionally scoped this as a client-side prototype to validate the concept before investing in backend or API infrastructure.

Key Product Decisions

1. Modular Sections Instead of Full Auto-Generation

 

PMs don’t always need a full PRD.
Sometimes they just need user stories or success metrics.

Giving users control over which sections to generate increases flexibility and mirrors real-world workflows.

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2. Structured Template Logic (Prototype Phase)

 

Instead of integrating a live AI API immediately, I used structured dynamic generation logic.

Why?

  • Zero API cost

  • Safe for public deployment

  • Faster iteration

  • Clear demonstration of concept

This allowed me to validate the workflow before scaling complexity.

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3. Fully Client-Side Architecture

 

The app runs entirely in the browser.

  • No backend

  • No database

  • No authentication

  • No API keys

This made deployment simple and eliminated operational overhead while showcasing product thinking and execution.

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4. Markdown Export

 

PRDs are often shared in GitHub, Notion, or internal documentation tools.

Supporting Markdown export makes the output immediately usable — not just visible.

Trade-Offs

This was a deliberate prototype, not a production SaaS product.

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Decision                                                         Trade-Off

No live OpenAI integration                                             Less contextual richness

Client-side only                                                                 No personalization memory

Structured logic vs generative AI                                    Predictable but less adaptive

No authentication                                                             Open public access

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I prioritized:

  • Speed of execution

  • Zero recurring cost

  • Concept validation

  • Demonstrability for portfolio

Over building production-grade infrastructure.

Deployment and Technical implementation

This prototype was built as a fully client-side web application using HTML, CSS, and JavaScript, and deployed via Netlify. The source code is publicly available on GitHub to demonstrate implementation transparency and architectural decisions. The application runs entirely in the browser with no backend dependencies, API calls, or database integrations, a deliberate choice to validate workflow value before investing in infrastructure.

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