Prompt Engineering Fundamentals for Developers

1-minute read
prompt-engineering • ai • llm • full-stack

Prompt engineering is the practice of designing inputs so LLMs produce reliable, useful outputs. Developers who treat prompts as code ship better AI features.

Structure Your Prompts

Use clear sections: role, context, task, format, and constraints.

You are a code reviewer for a TypeScript React codebase.
Context: PR diff below.
Task: List bugs, security issues, and style problems.
Format: JSON array of { severity, file, message }.
Constraints: Max 10 items. No speculation beyond the diff.

Few-Shot Examples

Include 2–3 input/output pairs for classification or extraction tasks. Examples beat lengthy instructions for format adherence.

Temperature and Parameters

Low temperature (0–0.3) for factual and structured tasks. Higher for brainstorming. Set response_format: { type: "json_object" } when you need parseable output.

Evaluation

Maintain a golden set of prompts and expected outputs. Regression-test when you change models or prompts.

Conclusion

Prompt engineering is not a substitute for validation and tests-it is the first layer. Combine with schema validation, retries, and human review for production systems.