Technique: Few-Shot Prompting (In-Context Learning)
Few-shot prompting provides examples within the prompt that teach the model a pattern before asking it to generate new output. This is one of the most impactful techniques in prompt engineering — research shows that even a single example can dramatically improve output consistency and quality.
The key is that your examples establish a pattern the AI should follow: naming conventions, formatting rules, tone, length, and style are all learned implicitly from the examples. The more consistent your examples, the more reliable the model’s output will be. This technique is sometimes called “in-context learning” because the model learns from context within the prompt itself.
When to use: When you need the output to follow a specific pattern, convention, or style that can’t be easily described in words alone. Product naming, classification with custom categories, content formatting, and code generation with specific patterns all benefit enormously from few-shot examples.