Few-Shot Examples
definition
Few-shot learning provides the model with examples of desired input-output pairs directly in the prompt, guiding its behavior through demonstration rather than instruction alone. This technique is one of the most reliable ways to improve output quality, especially for formatting, tone, and domain-specific conventions that are difficult to describe in words but obvious when shown.
Few-shot learning provides the model with examples of desired input-output pairs directly in the prompt, guiding its behavior through demonstration rather than instruction alone. This technique is one of the most reliable ways to improve output quality, especially for formatting, tone, and domain-specific conventions that are difficult to describe in words but obvious when shown. The key is selecting representative examples that cover edge cases, placing them where the model can reference them effectively (generally near the end of the context), and keeping them concise enough to not exhaust the context window budget. Few-shot examples are particularly powerful for agentic coding because they can demonstrate desired tool-use patterns — showing the agent exactly what a "good" tool call looks like in your system. This concept connects to system prompts where few-shot examples are typically embedded, context window budget for managing the token cost of examples, and chain of thought for combining examples with reasoning demonstrations.