The “Extract Prompt” pattern tackles a notorious quirk of large language models: their tendency to lose focus or “forget” instructions when overwhelmed by massive context windows. Instead of dumping an entire complex workflow into a single, massive prompt, this approach isolates intermediate tasks into separate, tightly scoped prompts. Doing so forces the model to attend strictly to the immediate problem without getting distracted by irrelevant history or unwritten team norms. It is a highly effective way to enforce rigorous software engineering patterns and maintain tight control over an AI agent’s execution sequence.

This paper illustrates this value using a classic Test-Driven Development (TDD) workflow, a process where models famously skip steps or write code out of sequence when left to their own devices. By extracting the TDD cycle into isolated prompts—asking the agent to first list test scenarios, write exactly one failing test, make it pass, and finally refactor—you effectively put the AI on rails. This granular decomposition stops the model from trying to solve the entire problem at once and ultimately yields much more robust, reliable solutions.