Architecture Prompts for Coding AI
Articulate complex code refactors and system constraints directly to Cursor and Claude.
The Workflow Challenge
Typing out extensive architectural context, data models, edge cases, and testing criteria for AI coding assistants like Cursor, Claude, or GitHub Copilot takes significant typing effort. Speaking complex requirements allows developers to provide rich, nuanced context with natural ease.
“refactor the user auth middleware to support both jwt bearer tokens and session cookies with redis session store and add unit tests with vitest for expired tokens”
“Refactor the user authentication middleware to support dual authentication schemes: JWT Bearer tokens and HTTP session cookies backed by Redis. Include Vitest unit test coverage validating expired token rejection and cookie rotation.”
How it works in 3 steps
Explain full architectural context
Dictate multi-sentence instructions with type signatures, testing frameworks, and file relationships.
Technical normalization & structure
Cleans up verbal pacing and organizes technical instructions into structured imperative prompts.
Pasted into the LLM chat input
The refined prompt is placed into Cursor or Claude prompt window ready for model execution.
Common questions
Does speaking prompts improve AI code output?
Yes. Because voice is lower friction, developers provide significantly richer context and constraints, reducing AI hallucination.
Can I use this completely offline with local models?
When transcription is fully local, audio stays on your device. In cloud mode, audio is sent to the provider you configure.
How does prompt transcription work?
Audio streams into the capture buffer and is converted into structured prompt text upon releasing the shortcut.
Try the Architecture Prompts for Coding AI Workflow in Early Access
Join the waitlist to receive access to macOS prototype builds equipped with this refinement preset.