Few-shot prompting is a technique where you include examples of desired input-output pairs in the prompt to teach the model the expected behavior. Rather than relying solely on instructions, you show the model what you want through demonstration.
A typical few-shot prompt follows this pattern: first, the system instructions define the task at a high level. Then, several examples show the input format, the expected output format, and any reasoning or transformation the model should apply. Finally, the actual user input is presented for the model to process.
Few-shot prompting is powerful because LLMs are strong pattern matchers. When given examples, models can infer complex formatting rules, domain-specific terminology, classification criteria, and output structures that would be difficult to specify in natural language instructions alone.
The number and selection of examples matters significantly. Research shows that as few as 2-3 well-chosen examples can dramatically improve performance, while poorly chosen examples can actually degrade it. Examples should cover the range of expected inputs, include edge cases, and be representative of the production data distribution.
Order effects also play a role. Models tend to weight more recent examples more heavily, so placing the most representative example last can improve consistency. For classification tasks, balancing examples across categories prevents the model from developing a bias toward the most frequently demonstrated class.
In production prompt management, few-shot examples are typically maintained as a separate prompt block that can be independently versioned and tested. This allows teams to refine examples over time based on evaluation results without modifying the core instructions.