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Prompt engineering playbook for programmers
Turn AI coding assistants into more reliable development partners
A good prompt frames the task, for example: “Refactor the following function to improve its readability and maintainability (reduce repetition, use clearer variable names).” Or “Optimize this algorithm for speed – it’s too slow on large inputs.” By stating specific goals, you help the model decide which transformations to apply . A real-world scenario: one developer asked an LLM to generate code to draw an ice cream cone using a JS canvas library, but it kept giving irrelevant output until they refined the prompt with more specifics and context . For instance, “Generate a complete Node.js app with authentication, a front-end in React, and deployment scripts.” Or even on a smaller scale, “Fix these 5 bugs and also add these 3 features in one go.” The AI might attempt it, but you’ll likely get a jumbled or incomplete result, or it might ignore some parts of the request.
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