Prompt Engineering Best Practices

Introduction

After thousands of production prompts, a few patterns emerge as universal best practices. Following them gives you a 90% head start on any new AI feature.

Beginner analogy: Like clean-code rules for prompts — boring but consistently effective.

Understanding the topic

Core concepts to understand:

• Be specific — replace 'good' with measurable criteria.
• Use delimiters (XML, ###) to separate sections.
• Place key instructions at the end (recency bias).
• Tell the model what to do, not what NOT to do.
• Provide examples for non-trivial format/tone.
• Force structured output (JSON) where parseable.
• Set temperature appropriately (0 = deterministic).
• Always evaluate on a fixed test set.
• Version-control prompts like source code.
• Add a fallback model + retry logic in production.

Syntax reference

Visual workflow / architecture:

Best-practice checklist:
  [✓] Clear role        [✓] Examples (few-shot)
  [✓] Output format     [✓] Delimiters
  [✓] Constraints       [✓] Eval test set
  [✓] Versioned         [✓] Fallback + retry
  [✓] Temperature set   [✓] Cost monitored

Real-world use

Anthropic, OpenAI, Vercel and Replit all publish official prompt-engineering guides distilling these exact lessons.

Best practices

• Start simple, iterate based on real failures.
• Maintain a 'prompt cookbook' for your team.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. List five prompt-engineering best practices.
• Q2. Why place key instructions at the end?
• Q3. Why tell the model what to do instead of what not to do?