Turning raw prompts into professional prompts that produce better AI output.
A full-stack AI workflow platform for prompt improvement, AI detection, and content humanization — powered by strong prompt rules and advanced language models. Also ships as a Manifest V3 Chrome Extension for real-time refinement inside ChatGPT, Gemini & Claude.
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The core workflow in one picture: take rough user input and return a structured, high-quality prompt.
Most people write weak prompts. They start with rough thoughts, vague instructions, or one-line requests, then keep retrying until the output feels usable. That wastes time and produces inconsistent results.
I wanted a product that makes prompt engineering practical: take raw user input, apply stronger structure and prompting rules, and return a polished prompt that performs better immediately — with supporting tools for AI detection and humanization inside the same dashboard.
Solo build — product, design, frontend, backend, AI workflow design, auth, and deployment. Designed the product around one idea: prompt quality should not depend on trial and error. Users start with rough input and get back something more structured, deliberate, and effective.
SuperPrompts converts rough prompts into professional prompts by applying stronger instruction design:
Under the hood, a deterministic prompt engine detects intent (coding, analysis, creative) and chooses the right template + rules — so quality stays consistent and doesn't depend on expensive API calls. Powerful language models handle the final refinement; strong rules do the structural heavy lifting.
Turn "summarize this email" into a professional, structured prompt with role, task, tone, constraints, and output format.
Analyze text for AI-like writing patterns and sentence-level signals.
Rewrite robotic text into more natural, human-sounding content.
The prompt optimizer is the core, but it grew into a whole workspace. Four optimization modes do the structural work, and one login unlocks a wider toolbox — all built into the same auth, credit, and billing system.
The same engine ships as a Manifest V3 Chrome Extension, enabling real-time prompt refinement directly inside ChatGPT, Gemini, and Claude — so users never have to leave the chat window to get a better prompt.
Tracked since the public beta opened in February 2026. Figures below are illustrative of the trend, not exported analytics.
Active users
cumulative · Feb–Sep 2026
Prompts generated
per month
Median time to a usable prompt
seconds · lower is better
Tasks completed
per week
// illustrative data — trend shape from product analytics, exact values rounded / synthetic
Harder than "call an API and print the result." The optimizer is the easy 20%. The other 80% was making it fast, cheap, consistent, and reliable enough that a stranger would trust it with their real work. These were the parts that took the most iteration:
Early versions sent the raw prompt straight to a model and hoped. Same input, different output every run. I moved the structural work into a rules engine that classifies intent (code / analysis / creative / writing) and applies a fixed template, so the model only does final polish. Quality stopped being a coin flip.
One provider is a single point of failure and a runaway bill. Requests route across OpenRouter, Groq, and NVIDIA NIM based on task type, current latency, and cost — with automatic failover when a provider is slow or rate-limited. Cheap, fast models handle the common cases; the expensive ones are reserved for hard ones.
ChatGPT, Gemini, and Claude each render their input box differently, ship UI changes without notice, and fight content scripts. The MV3 extension needs per-site DOM adapters, resilient selectors, and a shadow-DOM-safe overlay so a redesign on any one of them doesn't break the button.
A free tool that calls paid models needs guard rails. I built a credit ledger on Supabase with row-level security, per-account and per-IP rate limits, and abuse heuristics — tuned so a genuine user rarely hits a wall while automated scraping gets throttled fast.
Every extra tool — PDF suite, resume builder, translator, ChatPDF — is another surface to secure, meter, and keep working. Shared auth, a shared credit system, and a shared component library were the only way to ship this breadth without a team.
The biggest value wasn't just calling language models — it was designing a system that improves how people communicate with them. Better prompt structure consistently led to better outputs, and that made prompt engineering feel like a real product workflow instead of a manual habit.