Prompt engineering is the practice of designing and refining the instructions given to an AI model to produce desired outputs. Effective prompts provide clear context, specify the desired format, include examples, and anticipate edge cases.
For coding tasks, prompt engineering determines the quality of generated code. A vague prompt produces generic code. A prompt that includes the tech stack, coding conventions, error handling expectations, and the specific business requirement produces code that is closer to production-ready.
The best agentic workflows reduce the need for manual prompt engineering by automatically enriching prompts with task context, codebase information, and organizational standards.