What is Prompt Engineering?
Prompt engineering is the practice of carefully designing and optimizing the text prompts given to language models to elicit desired behaviors, outputs, or reasoning patterns. It encompasses techniques for structuring instructions, providing context, demonstrating desired formats through examples, and framing tasks in ways that leverage the model's capabilities effectively. Prompt engineering has become a crucial skill for working with LLMs, often determining whether an application succeeds or fails.
The discipline includes various techniques and patterns: clear instruction writing that specifies exactly what is desired, few-shot learning where examples demonstrate the task, chain-of-thought prompting that encourages step-by-step reasoning, role assignment that shapes the model's perspective and tone, output formatting specifications, and careful context management. Effective prompts often iterate through testing and refinement, as subtle wording changes can significantly impact model behavior.
Prompt engineering is fundamental to building AI agents and applications. System prompts define agent behavior and capabilities, task prompts structure individual operations, and retrieval prompts determine how models use retrieved information. As models become more capable, prompt engineering evolves from simple instruction writing to sophisticated orchestration of multi-step reasoning, tool use, and decision-making. The field continues to develop best practices as practitioners discover patterns that reliably improve performance across different models and tasks.