I Built a Coding Framework to Solve AI Inconsistency
Applying UX thinking and systematic problem-solving to make AI a reliable development tool.
Same Prompt, Different Code Every Time
I was using AI for development work, but I kept hitting the same frustrating wall: consistency was impossible. Same prompt, different sessions, different implementations.
Tailwind one session. Raw CSS the next. Same design, different code. No memory, no standards, no reliability.
I needed AI to work with me, not against me—like a consistent teammate, not a slot machine.
Claude Development Framework
Turn AI into a consistent teammate.
Stops "Why Did It Use Different Patterns?"
Every implementation follows your established standards—verified before a single line is written. The AI reads your conventions first, shows you the plan, and waits for approval before coding.
Stops Starting From Scratch
Your stack config, coding patterns, and architectural decisions are always loaded and enforced. No more re-explaining "we use X pattern for Y" in every new session—the framework remembers.
Stops Inconsistent Implementations
Every session follows the same workflow—no more randomness. Same task, same process, same quality checks. The AI can't skip steps or forget your standards.
Open source • MIT License • Free to use
From Guesswork to Certainty
The framework doesn't just improve code quality—it ensures AI executes exactly as your project is defined to work.
Without Framework
You:
"Create a button component"
Claude:
Immediately generates code...
• ❌ Arrow function (you use function declarations)
• ❌ Default export (you use named exports)
• ❌ Inline styles (you use Tailwind)
• ❌ Generic props (you have interface naming conventions)
• ❌ No TypeScript strict mode compliance
• ❌ Missing JSDoc documentation
Result: Code that works but doesn't match your project
With Framework
You:
"Create a button component"
Claude:
Shows standards check...
📋 Stack: Astro 5.0 + TypeScript strict + Tailwind
📋 Standards: react-standards.md, typescript-standards.md
📋 Will create: Button.tsx with ButtonProps interface
📋 Pattern: Functional component, named export, daisyUI classes
Wait for your approval...
You:
"yes"
Claude:
Generates code following YOUR patterns...
• ✅ Function declaration (matches your standard)
• ✅ Named export (matches your standard)
• ✅ Tailwind + daisyUI (matches your standard)
• ✅ ButtonProps interface (matches your convention)
• ✅ TypeScript strict mode compliant
• ✅ JSDoc documentation included
Result: Code that works AND matches your project exactly
The difference isn't just quality—it's consistency. The framework ensures every piece of code AI generates follows the exact patterns, conventions, and standards you've defined for your project.
The Recursive Quality Loop
Every task follows this 6-step workflow. It's what prevents "same prompt, different implementation each session."
Read Standards
The framework automatically loads your stack configuration and reads all relevant coding standards before generating any code. It examines official documentation for your specific framework version, identifies current best practices, and ensures Claude operates within your established conventions. This prevents inconsistent patterns and ensures every implementation matches your project's architectural decisions.
Show Plan
Before writing a single line of code, the AI presents a complete standards checklist showing exactly what it will build, which standards apply, and which files will be created or modified. You review the approach, verify the right patterns are being used, and approve the plan. This eliminates surprises and ensures alignment before any work begins.
Verify
After implementation, the framework runs comprehensive quality checks including code formatting, linting, type checking, build verification, and tests. It validates the code against 45+ quality criteria to ensure everything works correctly and follows established patterns. Any failures are caught immediately and fixed before proceeding.
Report Back
The system provides detailed verification results showing exactly what was checked and whether each quality gate passed. You get complete visibility into formatting status, lint results, type checking outcomes, build success, and test coverage. This transparency ensures you understand exactly what quality standards were met.
Wait for Approval
You review all the verification results and the completed implementation before committing anything. The framework prepares a properly formatted commit message following your version control standards, but waits for your explicit approval. You remain in control of what goes into your codebase.
Next Task
The framework maintains architectural memory across sessions, preserving knowledge of your stack, standards, and previous decisions. When you start the next task, it remembers everything—preventing the typical "same prompt, different implementation" problem. Every subsequent task follows the same quality loop with full context of what came before.
This prevents: "Same prompt, different implementation each session"
The framework ensures AI remembers your stack and follows your standards—every time.
This Is UX Thinking Applied to AI
Building this framework wasn't just coding—it was systematic problem-solving
Research
Identified the inconsistency problem through real usage
Document
Captured patterns and standards that work
Systematize
Built a framework to enforce consistency
Scale
Now works across all projects automatically
I approach every problem this way: understand it, solve it systematically, make it repeatable.
Want to try it out?
Open source and available for anyone to use. You can set it up in just a few minutes by following the README.
Open source • MIT License • Free to use