Translation

Writing Loops, Not Prompts

The article argues that developers and users benefit more from writing iterative loops (code logic, automation, multi-step processes) than from relying on single large prompts for AI models, emphasizing that breaking down tasks into loops improves reliability, control, and outcomes in AI-driven workflows.

Background

- The article argues that developers should solve AI problems by writing programmatic loops (e.g., retrying, iterating, chaining calls) rather than trying to craft the perfect prompt — a natural evolution of the shift from "prompt engineering" toward "AI engineering" with deterministic control flow. - The author, Rico, is a developer writing from personal experience, not a major company or research lab, but the piece reflects a growing sentiment in the AI engineering community as of 2025. - Prior context: Early LLM usage relied heavily on prompt engineering (tweaking wording), but practitioners have realized that robust applications need structured code around the model — validating outputs, retrying on failure, splitting tasks into smaller loops — treating the LLM as a fallible subroutine rather than an oracle.

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Writing Loops, Not Prompts

Background

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