What is Prompt Engineering?

Prompt Engineering is the craft of writing prompts that get you better AI outputs. Not just "write me code"—it's "You are an expert Python developer. Write a function that does X. Use type hints. Add docstrings. Return error codes." Techniques: clear instructions, personas, examples (few-shot), formatting, constraints. Good prompts = 10x better results from the same model. Cheaper than fine-tuning, faster than building custom models.

When Should You Use This?

Always. Every interaction with AI involves prompting. Invest time in prompt engineering when: building production AI features, quality matters, you call AI repeatedly, or you want consistent outputs. Start simple, iterate based on results. Techniques: be specific, add examples, use personas, show desired format, add constraints, break complex tasks into steps. Test prompts with multiple inputs.

Common Mistakes to Avoid

• •Too vague—"write code" vs "write Python function with type hints and error handling"
• •No examples—few-shot (2-3 examples) often doubles quality
• •Not iterating—first prompt rarely perfect, test and refine
• •Overly complex—sometimes simple prompts work better, start simple
• •Ignoring model differences—prompts that work on GPT-4 might fail on GPT-3.5

Real-World Examples

• •Bad: "Summarize this." Good: "Summarize in 3 bullet points, focus on key takeaways."
• •Bad: "Write code." Good: "As a senior dev, write Python function with types, docs, error handling."
• •Bad: "Classify this." Good: "Classify as positive/negative/neutral. Examples: 'I love it'→positive, ..."
• •Technique: Chain prompts—use output of prompt 1 as input to prompt 2 for complex tasks