Prompt engineering that actually ships
No theory. Just the structures, patterns, and techniques that make Claude, Cursor, GPT-4, and v0 execute on the first try. Written by Pedro.
How to Write Better Prompts for Cursor AI (That Actually Work First Try)
Most Cursor prompts fail because they are vague. Here is exactly how to structure prompts that make Cursor build what you meant, without back-and-forth.
Screenshot to Code: How to Turn Any UI Into a Working Prompt
You found a UI you love. Here is the exact method to extract every detail from a screenshot and turn it into a prompt that Claude or GPT-4 can build from scratch.
Stop Wasting Tokens: The Prompt Structure That Cuts AI API Costs by 60%
Vague prompts force AI to guess, generate more tokens, and still get it wrong. Here is the structure that eliminates all of that.
The Best Prompt Structure for Claude, v0, and Windsurf
Each AI coding tool interprets prompts differently. Here is what works specifically for Claude, v0, and Windsurf — with real examples.
Token Savings Data: How Structured Prompts Cut AI API Spend by 6–8×
First-party estimates from tknctrl usage patterns: vague prompt retry loops vs one-shot structured prompts on Claude Sonnet-class models.
tknctrl vs Writing Prompts Manually: When Automation Wins
A clear comparison of manual prompt engineering versus using tknctrl for role, stack, specs, and constraints — with time and quality tradeoffs.
The Cursor Prompt Template (Role, Stack, Specs, Constraints)
Copy-paste Cursor prompt template with role, stack, exact specs, interaction states, and constraints — plus how to fill it in under a minute.
Prompt Retry Rates by Structure: First-Party Estimates
First-party estimates of average correction rounds by prompt type — vague, partial specs, and full Role/Stack/Specs/Constraints — and what that means for token spend.