Effective prompting for developers

There's a big difference between "writing a prompt" and "designing a prompt." The first is what we do in the chat: one sentence, let's see what comes out. The second is what you need when that prompt lives inside your product and has to deliver consistent results thousands of times.

As a developer you already have the right instinct: a prompt is input to a system, not a casual conversation. These are the practices that make the most difference once you treat it that way.

1. Structure beats verbosity

A long prompt isn't a good prompt. A structured one is. Separate the parts so the model knows what each thing is:

Role: you are a code reviewer looking for security bugs.
Task: review the diff and list only real problems.
Format: a list; each item with file, line, and severity.
Constraint: don't comment on style or naming.
 
Diff:
<code here>

That separation — role, task, format, constraint, data — does more for quality than any adjective. The model stops guessing your intent.

2. Context goes last, stable content goes first

If you'll reuse the same preamble across many calls (fixed instructions, examples), put it at the start and leave what changes — the question, the user's data — at the end. This isn't just tidiness: it's what lets prompt caching work, making each call cheaper and faster. A prompt whose beginning changes on every request can't be cached.

3. Show examples, not just rules

Saying "respond concisely" is weak. Showing one example of the output you want is strong. Positive examples (do this) work better than prohibitions (don't do that):

Example of a good response:
{ "intent": "refund", "confidence": 0.92 }

The model imitates the format and tone of an example far better than it follows an abstract description.

4. Ask for structured output when you'll parse it

If your code is going to read the response, don't hand-parse free text. Ask for JSON with a schema and validate it. When the data has a fixed shape — a classification, an extraction — a strict schema saves you a whole layer of brittle parsing. Treat the model's output the way you always treated user input: validate at the boundary.

5. Be literal about what you DON'T want

Current models follow instructions more literally than earlier generations. That means two things. The good: if you say exactly what to do, it does it. What changed: the aggressive prompts of the past — "CRITICAL: you MUST always…" — now overreact. Dial it back. "Use the tool when it helps" works better than "ALWAYS use the tool."

6. Iterate with an evaluation, not vibes

This is what separates hobby prompting from production prompting. Don't tune a prompt by looking at one response and saying "looks good." Build a small set of cases — real inputs with their expected output — and measure it. When you change the prompt, run the set and see if it got better or worse. It's exactly a test loop, applied to natural language.

case 1: "I want my money back"     → intent: refund
case 2: "how do I change my password?" → intent: account
case 3: "your app is garbage"      → intent: complaint

Ten cases like these tell you more than a hundred eyeball reads.

What I take away

The jump in quality doesn't come from finding "the magic words." It comes from treating the prompt as software: structured, with ordered context, with examples, with validated output, and with an evaluation that tells you whether you're improving. Everything else is intuition, and intuition doesn't scale to thousands of requests.