🔮Resumancer

Product Requirements Document

📋 About This Document

This page serves as an example of my product documentation style. It uses Resumancer as a lightweight case study to showcase how I approach problem definition, user needs, solution framing, and the end-to-end execution of an AI-powered tool. While Resumancer is a personal side project, the format and structure here reflect the same methods I apply in my professional product management work.

⚠️ Problem

What problem are we solving?

In today's oversaturated job market, qualified candidates are getting lost in the large volume of applications being generated by a new algorithm-driven hiring ecosystem. Many job seekers feel stuck, and are tired of recycling the same resume endlessly, only to see very few results. Even strong applicants struggle to articulate what makes them unique, and are looking for strategies that would help them differentiate themselves in ways recruiters actually notice.

For whom?

Resumancer is built for job seekers across different industries who feel invisible in the application process. This includes:

  • People who are qualified but receiving little traction or interview callbacks.
  • Candidates who are looking to change industries but aren't sure how to translate their existing skills.
  • Applicants who want to find new and creative ways to make themselves more memorable or differentiated but don't know how to do it strategically.
  • People overwhelmed by the emotional burnout of job searching and looking for motivation, clarity, and confidence.

When do they experience this issue?

They feel this pain most acutely when:

  • Feeling demoralized by the repetitive, transactional nature of online job boards.
  • Applying to countless jobs and receiving very few requests to interview.
  • Trying to pivot roles or industries and struggling to articulate how their background fits.

What data, research, and feedback do we have?

Since this application was developed as my own creative method for standing out, the primary research I have at this time is my own lived experience as a job seeker. Before starting this project, I conducted rudimentary market research to see how many tools might exist for people experiencing the same problem. From what I uncovered, the primary method for solving this issue is working 1:1 with a career coach, which may be cost prohibitive for some applicants.

From a quantitative perspective, I have included basic page view analytics in this application. For future iterations, I would likely include:

  • Unique visitors
  • Time on site
  • Bounce rate
  • Ideas generated per session
  • Cards shared (or ideally saved) rate

Why is it important?

When you're struggling to find work, the process can feel demoralizing, destabilizing, and even frightening if money is tight. Career coaches are expensive, and not everyone has the resources to pay for personalized support. By creating a free, accessible tool that helps users generate new ways to position themselves, we give people a sense of control and momentum. It allows them to build something tangible and feel progress without additional financial strain.

Resumancer aims to empower people to:

  • Find their unique strengths.
  • Communicate their value more clearly.
  • Present themselves in memorable, human ways.
  • Access practical and creative ideas for marketability.
  • Infuse their materials with personal flare that feels authentic.
  • Receive personalized guidance that actually matches who they are.

💍 Proposal

How are we solving this issue?

Resumancer gives job seekers personalized, easy-to-use guidance that helps them market themselves more clearly, creatively, confidently, and inexpensively. Users input their target role, industry, and context, and the tool generates tailored ideas in a consistent tone based on their selected mood.

Technically, it's a lightweight Next.js app that sends a single optimized request to OpenAI's API and returns three structured idea paths. The interface is simple and performant, allowing people to quickly experiment, refine, and build momentum in their job search.

What alternatives did we consider?

This project began as a way for me to learn AI development while creating something memorable to share with recruiters. The first version was a tongue-in-cheek "career pivot generator" and more of a commentary on job-search frustration than it was a genuinely useful tool.

As I iterated, I realized it had the potential to be more than a comedic stand-alone application to help recruiters remember me. By reframing it as a marketability tool, the concept shifted from satire to something that could actually help users stand out, as well as be built upon in the future. Instead of just entertaining recruiters for my own job search purposes, it now helps people generate ideas for their own version of a "Resumancer": a unique, personalized and (sometimes) practical way to boost their own self-marketing.

Why did we land on this?

Resumancer gives users new angles to explore based on their own unique attributes. It meets people emotionally where they are (burnt out, optimistic, just trying to have fun) while still producing genuinely useful, professional outputs that make them more attractive candidates.

What is the general shape of this solution?

Resumancer is structured as a simple guided workflow:

  1. User provides inputs: target role, industry, additional context, and mood.
  2. System generates three ideas (all aligned with the chosen mood): creative, practical, and occasionally absurd.
  3. User can regenerate, refine, or use ideas to shape resumes, cover letters, personal branding, and job search strategies.

Have we considered performance, scalability, and cost?

Yes. The solution is intentionally lightweight and cost-controlled:

  • Backend: One API call per generation keeps costs predictable. No excessive chaining or embeddings. No heavy database until needed, which keeps infrastructure overhead low.
  • Frontend: Next.js 16 with Turbopack ensures fast dev and production builds. Minimal client-side libraries reduce bundle size.
  • Deployment: Vercel's edge network provides fast global load times without extra configuration. A static-plus-serverless hybrid approach allows the app to scale without manual intervention.
  • Caching (future): Potential future additions include caching responses for faster load times and lower token usage, especially for repeated or similar queries.

⏭️ Future Steps & Strategic Direction

Even though Resumancer is a personal project, there's clear room to evolve it into a more robust, user-centered experience. Potential next steps include:

1. Add lightweight analytics

Track unique visitors, time on site, bounce rate, ideas generated per session, cards shared, and returning users. This would help validate whether the tool drives real value for job seekers and identify where users drop off.

2. Expand the feature set

Potential additions include:

  • "Save this idea" or "Email to myself".
  • A simple idea history page powered by a small database.
  • A branded Resumancer profile page and PDF download that users can share with recruiters.
  • A longer, more in-depth guided version of each recommendation that helps users turn an idea into polished resume or LinkedIn copy.

3. Improve performance and scalability

Optimize prompt design, introduce minimal caching to reduce latency, and keep the infrastructure lightweight to maintain low operational costs even as usage grows.

4. Refine messaging and positioning

Clarify how Resumancer helps users stand out, and improve onboarding so users instantly understand what it does and how it benefits their job search. This may include a short tour, example inputs, and before-and-after style demonstrations.

5. Conduct small-scale user testing

Run quick, qualitative tests with a handful of job seekers to gather feedback on clarity, tone, and usefulness. Use this feedback to refine copy, adjust prompting, and improve UX.

6. Prepare for a broader share-out

Once V1 feels stable, share Resumancer on LinkedIn, in a portfolio, and with recruiters as a demonstration of product thinking, UX design, and applied AI. This can also serve as a living case study that evolves over time as I incorporate real feedback and usage data.