AI Coding Discipline — Why Structured Prompts and Contracts Matter

Setting Up AI Correctly

AI-assisted coding raises real, often unanswered questions: how do you configure the AI and its individual agents so everything works correctly without breaking code and logic that already exists? Refactoring must never break the structure that is already in the codebase.

What Matters Most to AI

• A precise, detailed prompt — the more clearly a requirement is described, the better it is executed.
• Breaking work into smaller, granular steps with verification after each one gives better results than one large prompt.
• Reducing hallucination — AI tends to pick the closest-sounding option by keyword match, even when it does not match the actual requirement.
• Embedding business logic and explicit "what must not break" constraints into the prompt produces better results than a prompt plus unit tests alone.
• Unit, integration and end-to-end tests exist to confirm that a given change did not break functionality outside the task's own scope.

What AITM Adds On Top of a Standard AI Agent

• Contracts, DOM elements, behavior chains and business-logic descriptions that a plain AI agent does not have access to.
• Tasks generated from Chat or Brainstorm use contracts, documentation and behavioral business logic to quickly locate the parts of the code that must not change, and inject the relevant business logic into the prompt before the AI touches anything — producing a prompt tailored to the AI with minimal hallucination and the best possible implementation.
• Prompts also receive skills and specific contracts, letting a task be steered precisely toward existing coding patterns and existing business logic, improving how well the change fits the current code.
• Every pipeline task is generated according to its request type — Bug, Enhancement, or Feature — and is executed and verified differently in the pipeline for each type, keeping code quality consistently high.
• Large requirements are split into smaller, dependent tasks that build on each other; the final task in the chain verifies that all of them were created correctly and precisely.
• Large or illogical changes cause the task to stop, requiring a human (or a stronger model) to confirm the change is actually correct before it proceeds.
• The Architect step gathers what will change and where, reviews code-coverage metrics, and writes down precisely what is happening — nothing more, nothing speculative.