PromptArchitect
Prompts are code. Treat them that way.
The Problem
AI prompt development is trial-and-error chaos. Teams paste prompts into ChatGPT, tweak words randomly, and have no version history, no A/B testing, no way to know if a prompt that works in a demo will work in production. Prompt engineering needs the same discipline as software engineering.
The Approach
A prompt engineering and architecture platform that brings version control, structured testing, and production monitoring to AI prompt development. Design prompts with templates, test them against evaluation datasets, track performance metrics, and deploy with confidence.
Status
In Development
Category
AI & Automation
Founded
2024
Role
Founder & Developer
Market
AI engineering teams, product teams using LLMs, prompt engineers, AI consultancies
Team
Solo founder
Tech Stack
React, Vite, Supabase, Google GenAI
Deep Dive
PromptArchitect treats prompts as first-class engineering artifacts — not disposable text strings.
The Problem with Current Prompt Development
Most teams develop prompts the way developers wrote code in 1995: no version control, no testing, no deployment pipeline. A prompt that works perfectly in a playground fails silently in production because the input distribution is different, the model was updated, or edge cases were never tested.
What PromptArchitect Provides
Version-controlled prompt templates with variable injection. Evaluation datasets that test prompts against known-good outputs. Performance dashboards that track accuracy, latency, and cost per prompt across model versions. Collaboration features so teams can review and approve prompt changes before they reach production.
Architecture
Built with React and Vite for the frontend, Supabase for persistent storage and real-time collaboration, and Google GenAI for the evaluation pipeline. The platform itself uses the prompt engineering discipline it teaches — every system prompt in PromptArchitect is version-controlled and tested against its own evaluation framework.
Milestones