AI Prompt & Code Refactor
Suggested by @maayavicoderStructured Templates
Refactor raw inputs into structured prompts containing XML tags, behavioral roles, and few-shot examples that align with LLM training weights.
Model Alignments
Target prompts specifically for leading LLM architectures like Claude, ChatGPT, and Gemini to maximize parsing accuracy.
Privacy Heuristics
The refactoring engine generates optimized prompts client-side using rule-based parsing heuristics to keep your code and prompts completely secure.
How to Refactor Prompts and Code
Input Instructions
Paste your raw query, coding instruction, or configuration goal in the editor prompt box.
Choose Platform
Select your target AI model and the output length profile (Concise, Balanced, or Advanced).
Configure Domain
Choose the appropriate domain mode (Coding, UI/UX, Research, Technical, Creative) to inject standard constraint files.
Copy Prompts
Click 'Enhance Prompt' to output the refactored text template, ready to copy and run.
Technical Specification Matrix
| AI Framework | Primary Structure Pattern | Key Injected Claims | Optimal Domain |
|---|---|---|---|
| Claude (Anthropic) | Nested XML block separators (e.g. <instructions>, <context>) | System roles, behavioral parameters, task scopes | Complex coding, architectural layouts, detailed critiques. |
| ChatGPT (OpenAI) | Clear markdown sections with bold parameter tags | Step-by-step logic checks, target output structures | General questions, scripts generation, formatting data. |
| Gemini (Google) | Structured context blocks, explicit constraints list | Inferred specifications, multi-modal alignment files | Creative writing, structured schema validation, fast lookups. |