Today's concept: prompt engineering for code generation
The same AI model can write wildly different code depending on whether you show it an example or just describe what you want.
Prompt engineering is how you talk to an LLM to get reliable, predictable code output. Think of it like this: if you're pair programming with someone, a five-minute conversation where you show them an existing pattern usually gets you better code than a ten-minute spec. The model works the same way, it learns from structure (your instructions), but much better from examples (actual code it can mirror).
Quick check before you scroll: If you want an LLM to generate React components in your specific style, what's more likely to work: (a) a detailed spec of your component rules, or (b) two example components showing your exact pattern?
Full breakdown + the answer: frankduah.me/learnings/2026-07-21-prompt-engineering-for-code-generation
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The answer
(b), examples. The model learns by pattern-matching to existing code far better than by parsing rules. This is why prompt engineering in LLM apps is so critical: you're essentially training the model's output through demonstration, not instruction.