CatchCase

Mastering prompt engineering: a developer's guide

Prompt engineering has evolved from a hobbyist trick into an everyday engineering discipline. When LLMs power features from code generation to decision support, writing predictable, structured and reliable prompts matters as much as writing clean backend logic.

This guide covers the techniques we see make the biggest difference for developers.

1. The anatomy of a production-ready prompt

A loose question works for a quick chat. For anything your code depends on, use a deterministic structure and separate the parts clearly — with XML-style tags or Markdown headings:

  • Role: the expertise you need — "You are a senior PostgreSQL performance engineer."
  • Context: the relevant code, schemas or documentation, and nothing else.
  • Instructions: the task itself, stated as concrete actions.
  • Constraints: hard boundaries — "Don't add dependencies", "Keep it O(n)".
  • Output format: exactly what should come back — a JSON object, a single code block, a diff.
<role>You are a senior PostgreSQL performance engineer.</role>
<context>
  <schema>…</schema>
  <query>…</query>
  <explain_analyze>…</explain_analyze>
</context>
<instructions>Find the main bottleneck and propose one index or rewrite.</instructions>
<constraints>Postgres 16. No schema changes other than indexes.</constraints>
<output>A short diagnosis, then the SQL in one code block.</output>

2. Few-shot examples and explicit reasoning

When a task needs a particular format or a non-obvious judgement, zero-shot prompts are often inconsistent. Two techniques fix most of it.

Few-shot prompting

Include one to three complete input → output examples. Nothing teaches a model your formatting, naming conventions or tone faster. Make the examples varied; three near-identical examples teach the model to copy them.

Asking for the reasoning first

For logic-heavy tasks, ask the model to work through the problem before it answers — "First list the cases you need to handle, then write the function." Many current models reason internally on their own, but asking for an explicit plan still helps you review the answer and catch a wrong assumption early.

3. Structured outputs

If your backend parses model responses, free-form text is a liability. Ask for JSON that matches a schema, and prefer the structured-output features of your provider's SDK when they exist. A TypeScript interface pasted into the prompt is a compact, readable way to describe the shape you expect:

interface Review {
  verdict: 'approve' | 'request_changes';
  issues: { file: string; line: number; problem: string }[];
}

Validate the result anyway. Treat a response that fails validation like any other failed external call: retry once with the validation error included, then fall back.

4. Treat prompts like code

  • Version them. Keep prompts in files under source control, not in string literals scattered around the codebase.
  • Test them. A handful of fixed inputs with expected properties catches regressions when you change the prompt or the model.
  • Log inputs and outputs (without personal data) so you can see what the model actually received when something goes wrong.

The cases in the CatchCase library follow the same structure — open any of them to see a full prompt, then run it with your own inputs.