Jan 2026·9 min

Prompt Engineering for Developers: Beyond 'Be Concise'

Structured prompting techniques, chain-of-thought patterns, and tool-use design that turn LLMs from chatbots into reliable software components.

Prompt EngineeringLLMsDeveloper Tools

Treat a prompt like a function signature: typed inputs, a declared output contract, and tests. Everything else is folklore.

Make the output a schema, not a hope

Structured outputs remove the entire class of bugs where you parse prose with a regex at 3 a.m.

const Ticket = z.object({
  severity: z.enum(["low", "medium", "high", "critical"]),
  component: z.string(),
  summary: z.string().max(120),
  steps: z.array(z.string()).min(1),
});

const res = await ai.chat({
  model: "google/gemini-2.5-flash",
  messages: [
    { role: "system", content: "Classify the bug report. Use only the provided enum values." },
    { role: "user", content: report },
  ],
  response_format: { type: "json_schema", json_schema: { name: "ticket", schema: toJsonSchema(Ticket), strict: true } },
});

const ticket = Ticket.parse(JSON.parse(res.choices[0].message.content));

Version prompts like code

export const PROMPTS = {
  "classify@3": {
    system: "Classify the bug report. Prefer 'medium' when evidence is thin.",
    temperature: 0,
  },
} as const;

// Log the version with every call so a quality regression is a diff, not a mystery.
logger.info({ prompt: "classify@3", tokens: res.usage.total_tokens });

Techniques that survive contact with production

–Put the instruction before the data, and delimit the data clearly.
–Give two or three examples of the hard cases, not the easy ones.
–Ask for reasoning in a field you discard, rather than banning it — quality drops when you forbid thinking.
–Set temperature 0 for anything a machine will consume downstream.
–Keep an eval set of 30–50 labelled cases; run it on every prompt change in CI.

'Be concise' is not a technique. A schema, an eval set, and a version number are.

// keep reading

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