The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
CoverFlow AI
1. Short Explanation
CoverFlow (GenLetter AI) is a production-grade AI-powered cover letter generator built with Next.js 15 that helps users instantly create personalized, professional cover letters from structured inputs.
It is designed for job seekers, professionals, and freelancers who want to generate well-written, tailored cover letters in seconds instead of starting from scratch. The system combines form-driven input, AI prompt engineering, and document export into a seamless writing-to-download workflow.
2. Tech Stack
Next.js 15.5 (App Router)
React 19
TypeScript 5.7
Google Gemini 1.5 Flash (@google/generative-ai)
Zod
react-hook-form
shadcn/ui (New York style)
Radix UI (40+ primitives)
Tailwind CSS
Framer Motion
next-themes
docx
file-saver
ReactMarkdown
3. Problem Solved
Writing a strong cover letter is time-consuming, repetitive, and often difficult for users who struggle to translate their experience into professional language.
Most users either:
Copy generic templates that feel impersonal
Spend hours writing and rewriting from scratch
Or fail to structure their experience effectively for recruiters
GenLetter AI removes this friction by turning structured personal input into a fully written, polished cover letter instantly.
4. Results / Key Features
Instant AI-generated cover letters tailored to job role and company
Structured 9-field input system for precise personalization
High-quality formatting output suitable for real job applications
Clean split-screen live preview for immediate feedback
Export-ready DOCX generation for direct submission
Professional prompt engineering enforcing tone, structure, and length constraints
Responsive, modern UI with smooth animations
Dark/light mode support for accessibility and UX comfort
5. Problem
Job applicants often struggle to create cover letters that are:
Personalized enough to stand out
Professionally structured
Fast to generate across multiple applications
Consistent in tone and formatting
Existing solutions are either overly generic templates or manual writing tools with no intelligence layer.
6. Solution
GenLetter AI introduces a structured AI writing pipeline:
User fills a 9-field form (name, role, company, experience, skills, etc.)
Data is validated using Zod and react-hook-form
A structured prompt template is sent server-side to Gemini 1.5 Flash
The AI generates a formatted, multi-paragraph cover letter (strict output rules enforced)
The result is rendered in a live split preview interface
User exports the final document as a professionally formatted DOCX file
This creates a fast, deterministic, and consistent AI writing system instead of unpredictable freeform generation.
7. Features
9-field structured input form (fully validated with Zod)
AI cover letter generation using Google Gemini 1.5 Flash
Strict prompt engineering (tone, structure, paragraph limits, formatting rules)
Live split-panel preview (input + rendered output)
Markdown rendering with ReactMarkdown
DOCX export with A4 formatting, margins, and professional typography
File download handling via file-saver
Responsive UI built with shadcn/ui + Tailwind
Smooth page transitions and Framer Motion animations
Dark/light theme support via next-themes
Landing page with Hero, How It Works, Features, and CTA sections
8. Stack
Frontend
Next.js 15 (App Router)
React 19
TypeScript 5.7
Tailwind CSS
shadcn/ui (Radix-based components)
Framer Motion
ReactMarkdown
Backend / Server Layer
Next.js Server Actions
Google Gemini API integration (@google/generative-ai)
Structured prompt engine (template-driven AI output control)
AI / LLM
Google Gemini 1.5 Flash
Prompt engineering enforcing:
3–4 paragraph structure
<400 word limit
Professional tone
Plain text output formatting
Forms & Validation
react-hook-form
Zod schema validation (9-field structured input model)
Export System
docx (DOCX generation with formatting rules)
file-saver (client-side download handling)
Auth (Scaffolded / Not Active)
next-auth v4 installed
useUser hook present
Not enforced in current app flow
Deployment
Vercel deployment configured (vercel.json present)
Ready for serverless deployment
Other Tools
ESLint
PostCSS
Vite-style modern build optimizations (via Next.js tooling)
9. My Role
As the developer of GenLetter AI, responsibilities included:
Designed full application architecture using Next.js App Router
Built structured AI prompt system for deterministic cover letter generation
Integrated Google Gemini 1.5 Flash via secure server-side execution
Developed 9-field validated form system using Zod + react-hook-form
Built responsive UI using shadcn/ui, Radix primitives, and Tailwind CSS
Implemented live preview system with Markdown rendering
Developed DOCX export engine with professional formatting rules
Designed landing page and user onboarding flow (Hero → Features → CTA)
Implemented theme system with dark/light mode support
Structured application for future scalability (auth + database scaffolding)
10. Live URL
11. GitHub
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
I’ve been quietly building my presence on Contra, contributing consistently and growing my discovery score to 72%🚀 🚀 🚀
I specialize in frontend development, CI/CD pipelines, email automation pipelines, funnel design, CRM management such as Kajabi, Systeme.io, Gohighlevel, Clickfunnels, Landpages, SamCart, ThriveCart, Keap, Webinar Jam, ActiveCampaign and Kit previously ConvertKit.
Welldone 👍
Keep it up!
AI Dating App
AI-powered dating companion that helps users improve their dating profiles, generate personalized conversation starters, and craft engaging replies using AI. It acts like a virtual “dating wingman,” helping users get better matches and more meaningful conversations on dating apps.
- AI matchmaking analyzes user interests, behavior, and preferences to suggest highly compatible matches.
- Smart conversation starters and AI-generated prompts help users break the ice and improve engagement.
- AI-based profile verification and moderation detect fake profiles, scams, and inappropriate content for safer dating.
- Personalized recommendations adapt over time based on swipes, chats, and interaction patterns to improve match quality.
- Sentiment and compatibility analysis provide insights into communication styles and relationship potential.
Excellent UI!
CasePrepAI: Insurance Case Management System
CasePrepAI was a workflow simplification and operational clarity project disguised as an AI product.
The core problem was not the lack of features, it was the amount of cognitive overload insurance teams faced while handling complex case preparation workflows across fragmented systems, documents, communication channels, and repetitive manual processes.
Most insurance workflows are operationally dense. Teams spend significant time gathering information, reviewing case details, organizing documentation, and preparing reports across tools that were never designed to work together cohesively.
My role was to rethink how AI could reduce operational friction without disrupting critical human decision-making.
Instead of designing AI as a standalone assistant, I approached the platform as a collaborative workflow system:
What tasks are repetitive?
Where do users lose time?
Which decisions require confidence and visibility?
What should AI automate versus support?
This led to a more structured, AI-assisted experience focused on speed, clarity, and workflow continuity.
I redesigned the information architecture to better support high-volume case management, simplified complex navigation paths, and introduced interaction patterns that made large datasets and case histories easier to scan, prioritize, and act on.
One of the key judgment calls was intentionally reducing unnecessary AI exposure across the product. In industries like insurance, trust and clarity matter more than novelty. Rather than forcing conversational AI into every workflow, I focused on embedding intelligence contextually where it improved efficiency, summarization, recommendations, and preparation speed.
The result was a platform direction that felt less like a traditional enterprise tool and more like an intelligent operational workspace designed around how insurance teams actually prepare, review, and manage cases under pressure.
More importantly, the project demonstrated my approach to product design:
I simplify complex systems, align AI with real user workflows, and design products that improve operational decision-making, not just visual interfaces.
Trending
Claude
Claude has entered the design space. How are you using Claude Design?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
portfolioreview
The best portfolios tell a story, not just show a grid. Share yours for feedback.
freelancerlife
Freelancer life is wins, pivots, and everything in between. What’s yours right now?