Prompt engineering encompasses the techniques and principles for designing inputs that reliably produce high-quality LLM outputs. It combines psychology, technical understanding, and iterative refinement to bridge human intent and machine interpretation.

Core principles:

• Clarity over brevity - Explicit instructions outperform implicit assumptions
• Role assignment - Define the model’s perspective and expertise level
• Output formatting - Specify structure, tone, and constraints upfront
• Iterative refinement - Test, measure, and adjust based on actual outputs

Example pattern:

Role: You are [specific expertise]
Task: [Clear objective]
Context: [Relevant background]
Constraints: [Format, length, tone]
Output: [Desired structure]

The difference between “Write code for user authentication” and a well-engineered prompt can be 10x improvement in output relevance and accuracy.