What This Research Area Covers
Research in this area studies how the structure and phrasing of an instruction to a model affects its output, including systematic techniques like few-shot examples, chain-of-thought prompting, and structured output formatting.
Why It Matters
How a model is prompted has a measurable, sometimes dramatic, effect on output quality and reliability without changing the underlying model at all, making this a low-cost lever for improving real-world AI system performance.
Current Research Directions
Systematic study of which prompting techniques generalize across models and tasks, automated prompt optimization methods, and understanding why certain prompt structures reliably improve performance are active areas.
Related Pages
Frequently Asked
Is prompt engineering considered a rigorous research area?
Yes, there's a growing body of systematic research studying which techniques reliably improve output across models and tasks, beyond informal trial and error.
What is chain-of-thought prompting?
A technique where a model is prompted to work through its reasoning step by step before answering, often improving accuracy on complex problems.
Where can I find practical prompting techniques, not just research?
See our Prompt Engineering guide for the applied version of this research.
Where can I find ready-to-use prompts?
See our Prompts library.