Context Beats Cleverness
The most common reason a model gives a bad answer is that it lacked information, not that the prompt wasn't clever. Supply the relevant code, the error in full, the versions you're on, and the constraint you care about. A plain prompt with great context outperforms a "magic" prompt with none.
Show, Don't Just Tell (Few-Shot)
When you want a specific style or format, include one or two examples of input → desired output. Few-shot examples anchor the model far more reliably than adjectives. "Format like this: [example]" beats "format it nicely" every time.
Constrain the Output Shape
State exactly what you want back: a unified diff, a single TypeScript file, a JSON object with these keys, or "code only, no explanation." Explicit format instructions are honored reliably and save you from reshaping the response by hand.
Decompose Big Asks
A vague "build me a dashboard" invites a sprawling, wrong answer. Break it down: schema first, then the API, then one component. Smaller steps are easier for the model to get right and easier for you to verify before moving on.
Ask for Reasoning on Hard Problems
For debugging or design trade-offs, ask the model to think through the options before answering — "list possible causes ranked by likelihood, then recommend the fix." This surfaces the reasoning so you can spot a wrong assumption, instead of getting one confident answer with no justification.
Make It Check Its Own Work
Add a verification step to the prompt: "after writing the function, list the edge cases it handles and any it doesn't." Self-review catches a surprising number of off-by-one and null-handling bugs before they reach your editor.
If the first answer is close, refine it ('good, now handle the empty array case') instead of rewriting the whole prompt. Conversation is cheaper than re-specification and keeps the good parts.
Tell It Who To Be — Briefly
A short role frame ("you are reviewing this for security") sharpens focus. Keep it to a line; long personas add noise without improving code-quality answers.
Frequently Asked Questions
Does prompt engineering still matter with smarter models?
Yes. Better models forgive sloppy prompts more, but context, examples, and clear output constraints still produce materially better results and fewer iterations.
What's the single most effective technique?
Supplying complete, relevant context. Most bad answers come from missing information, not poor phrasing.
Is chain-of-thought useful for coding?
For debugging and design decisions, asking the model to reason through options first helps you catch wrong assumptions. For simple completions it's unnecessary overhead.