What is Prompt Engineering?
The practice of designing inputs to AI models to reliably produce desired outputs.
Definition
Prompt engineering is the practice of crafting instructions (prompts) for AI language models to reliably produce outputs that meet specific requirements. It includes techniques like few-shot examples (showing the AI examples of the desired output format), chain-of-thought prompting (asking the model to reason step by step), and system prompt design (setting the model's context and constraints). Good prompt engineering significantly improves output quality and consistency.
Example
Instead of prompting 'summarize this support ticket', a well-engineered prompt says: 'Classify this support ticket into one of [billing, bug, feature-request, access, onboarding]. Respond with only the category label and a one-sentence rationale. If unclear, classify as billing. Ticket: ...'
Prompt Engineering vs fine-tuning: What's the difference?
The practice of designing inputs to AI models to reliably produce desired outputs.
Prompt engineering changes what you tell the model at inference time. Fine-tuning changes the model's weights through additional training. Prompt engineering is faster and cheaper; fine-tuning produces more consistent specialized behavior.