Stop Burning Tokens: The Contract-First Prompting Blueprint No One Talks About

My site: https://natebjones.com
My substack: https://natesnewsletter.substack.com/

Takeaways
1. Intent Gap Drives Prompt Failures: Most prompts collapse because we never transmit our true intent; human language leaves too much room for misinterpretation by the model.
2. Contract-First Prompting: Treat the LLM like an engineering partner—draft a “contract” that defines mission, success criteria, and guardrails before any work starts.
3. Structured Clarification Loop: The prompt forces the model to list every unknown, then ask one question at a time until it reaches 95 % confidence it can deliver accurately.
4. Echo Check for Alignment: A single crisp sentence—deliverable, must-include fact, hardest constraint—lets me confirm we’re synchronized before locking scope.
5. Token-Efficient Precision: Lengthy system prompts aren’t required; a clear sequence of steps (gap listing, targeted Q&A, contract lock) achieves deep alignment with minimal tokens.
6. Domain-Agnostic Utility: Whether I’m summarizing 365 years of Balkan history or scoping a multi-platform livestream comment tool, the same framework extracts fuzzy intent and turns it into an executable work order.

Quotes
“Almost every prompt that fails fails because intent wasn’t clearly communicated.”
“Giving the LLM free rein to ask scattershot clarifying questions is an unprofessional way to handle ambiguity.”
“All we’re doing is listing the gaps to goal, digging until 95 % confidence, then locking the contract.”

Summary
I argue that unclear intent—our inability to translate rich domain knowledge into precise instructions—is the root cause of failed prompts. My solution is contract-first prompting: assign the model a mission, force it to enumerate missing facts, and interrogate me step-by-step until it’s 95 % certain it can deliver. An echo check locks the agreement, after which the model builds and self-tests the work. I demonstrate the method on gnarly tasks—from a 500-word Balkan history overview to a livestream comment aggregator—showing how a lean, structured prompt reliably converts vague ideas into high-quality output.

Keywords
contract-first prompting, intent gap, LLM alignment, clarification loop, echo check, prompt engineering, token efficiency, AI workflow, structured prompts, work order