ai
Prompt Engineering
The craft of designing prompts that get reliable, high-quality output from AI models.
Definition
Prompt engineering is the practice of designing prompts that consistently produce good output from LLMs. It includes: providing clear role and context, specifying output format, including examples (few-shot), chaining steps for complex tasks, and iterating based on outputs. Good prompts are reusable assets - they should be versioned, tested, and stored. For repeatable AI workflows in a business, prompt quality is what separates 'AI sometimes works' from 'AI reliably works'.
In your business
- →Save and version your best prompts - they're reusable assets, not throwaway lines
- →Include 2-3 examples in the prompt for tasks where format matters
- →Test prompts on edge cases - a prompt that works 80% of the time is fragile in production