Architext AI | Devpost

Inspiration

In software development, the journey from a brilliant idea to the first line of code is often a tredious one. As developers, we've all felt the analysis paralysis that happens when facing a new project. Which tech stack is best? How do we design for scale, cost, or both? What are the security best practices for this library or framework? I came across this article, "Technical Design Documents in a Nutshell," and came across something that really resonated with me. Jumping straight to coding a project without a plan is one of the most common mistakes, leading to poor architectural choices and costly refactoring down the line.

This problem affects everyone, from junior developers overwhelmed by the sheer number of options to senior engineers bogged down by the tedious process of creating design documents. Architext AI was born from this shared frustration. I was inspired to build a tool that acts as an AI-powered system architect, bridging the gap between concept and execution and empowering any developer to start their project with a solid, well-documented foundation-regardless of skill level.

This is what inspired me to create Architext.

What it does

Architext AI transforms a simple product idea into a comprehensive, developer-ready Technical Design Document (TDD). It guides the user through a structured, conversational discovery process to flesh out the technical requirements of their application. Forget about the long, drawn-out process of prompt engineering an LLM to create your own design document, Architext brings the questions to you.

Architext also significantly reduces token cost compared to the back-and-fourth of creating a design document with an LLM, and on-average uses ~2,000 tokens per document creation.

The user flow is simple but powerful:

Initial Idea: The user provides a high-level description of their app idea (e.g., "A real-time chat app with AI-powered moderation").
Intelligent Q&A: The model analyzes the idea and asks three to five targeted clarifying questions to cover crucial aspects like scale, architectural priorities, and core data models.
Final Clarification: The user has a final opportunity to add any other constraints, preferences, or context.
TDD Generation: With the gathered information, Architext generates a complete Technical Design Document (TDD), formatted in Markdown for clarity and ease of use, ready to be copied and shared with a development team.

How it was built

Architext AI is a full-stack application built with a modern, scalable tech stack, designed for a seamless user experience.

Frontend: I used Next.js with TypeScript for a fast, type-safe, and responsive user interface. The UI was built using Tailwind CSS and the excellent shadcn/ui component library.
Backend: The backend is a FastAPI application, running as a serverless function on Vercel. This API handles the core logic of the AI interaction.
AI Orchestration: I utilized LangChain to manage the conversational flow and interact with the Google Gemini API, which powers the question generation and the final TDD creation.
Deployment: The entire application is deployed on Vercel, providing a robust and scalable platform for both the frontend and our serverless Python backend.

Challenges I ran into

Building a polished, end-to-end AI orchestration application (my first) definitely came with its share of challenges.

Firstly, this hackathon was 12 hours long, and my second one at that. Being a solo developer for this one really increased the pressure on me, but I was able to create a polished, viable product.

One of the biggest hurdles was prompt engineering. Crafting prompts that could consistently guide the AI to ask relevant, non-generic questions and then synthesize the user's answers into a coherent technical design document required significant iteration and refinement.

Another challenge emerged during deployment. This is one of my many projects deployed on Vercel, but I've never deployed an app using a FastAPI backend with a Next.js frontend. This came with a slew of challenges in itself, as I had to think quick on how to utilize a backend within the same project without deploying the backend as a separate service. I learned that Next.js (Vercel) allows Python to be ran as serverless functions, which I used to my advantage to bundle my deployment into one package.

Finally, ensuring a smooth user experience for the multi-step wizard required a lot of fine-tuning. I spent considerable time polishing the UI—conditionally rendering buttons and progress bars, creating consistent loading states, and adding toast notifications—to make the process feel intuitive and professional.

Accomplishments that I'm proud of

I'm incredibly proud of creating my first AI orchestrated, fully functional, end-to-end application that directly addresses a significant pain point for the developer community, in 12 hours. Building a tool that automates the often-tedious process of architectural planning and documentation feels like a major win.

Successfully implementing the dynamic, conversational AI wizard was a key accomplishment. It feels less like a static form and more like a genuine interaction with a system architect, as none of the fields are pre-loaded. The model comes up with these questions itself.

What I learned

This project was a tremendous and rewarding learning experience, as it marked my first-ever venture into building an AI-powered application. I'm incredibly proud that I was able to successfully create a functional and polished end-to-end product using LangChain and the Gemini API for the first time.

I also learned the art and science of prompt engineering for AI models within projects— guiding a large language model to produce structured, reliable outputs. On top of the AI work, the deployment process taught me invaluable lessons about CI/CD pipelines, particularly the nuances of handling hybrid projects with platform-specific dependencies on Vercel.

Beyond the technical skills, this project reinforced the importance of solid fundamentals. I gained a much deeper appreciation for how a well-defined Product Requirements Document (PRD) can keep a project on track, after creating my own and using it in this application. Building Architext AI from the ground up solidified the principles of thoughtful design and documentation, proving that a good plan is the most critical first step to building great software.

What's next for Architext AI

This is just the beginning for Architext AI. I have several exciting enhancements planned for the future:

Prototype Generation: Taking the final TDD and using an AI agent to generate boilerplate code, including file structures, API endpoint shells, and more.
TDD Editing: An interface for users to manually edit the generated TDD before finalizing it.
Export Options: The ability to export the TDD as a PDF or Word document for easier sharing and integration into existing workflows.