Practice · Established · Beginner

Prompt Engineering

The practice of designing inputs that reliably get useful, correct and well-formatted output from a generative model.

What Prompt Engineering is

Prompting is interface design for a probabilistic system. A good prompt supplies role, context, constraints, examples and an explicit output format, and states what to do when information is missing.

How it works

Effective patterns include few-shot examples, step-by-step decomposition, explicit output schemas, negative constraints, and separating instructions from data to reduce injection risk. Prompts are versioned and evaluated against a test set like any other code.

Why it matters

The same model can be nearly useless or genuinely reliable depending on the prompt, and prompt quality is the cheapest lever available before touching models or fine-tuning.

Common uses

→Assistant and agent system prompts
→Structured data extraction
→Content generation at scale
→Evaluation harnesses

Strengths

✓No training cost
✓Immediate iteration
✓Portable across models with adjustment

Watch for

✓Brittle across model versions
✓Long prompts add cost
✓Vulnerable to prompt injection

Continue exploring

Retrieval-Augmented Generation Fine-Tuning Prompt Injection Temperature