What Are Some of the Aspects to Keep in Mind While Using Few-Shot Prompting?

Few-shot prompting has emerged as one of the most powerful techniques for interacting with large language models (LLMs). By providing just a few examples, you can guide the model toward producing the kind of output you’re aiming for—without any fine-tuning. But like any powerful tool, few-shot prompting needs to be used thoughtfully.

Here are some important aspects to keep in mind when using few-shot prompting effectively:

1. Clarity and Quality of Examples

Your examples are the foundation of the model’s response. If they are vague, inconsistent, or ambiguous, the model will likely return inconsistent results. Use clean, unambiguous, and well-structured examples that closely resemble the format and tone you want the model to follow.

Tip: If your output needs to follow a strict format (e.g., JSON, tabular, code), make sure your examples mirror that exactly.

2. Relevance of Examples to the Task

Make sure the examples you provide are directly aligned with the task you're asking the model to perform. Using irrelevant or overly generic examples can lead to drifting behavior or undesired outputs.

Example: If you're asking the model to summarize technical papers, don’t provide examples summarizing novels.

3. Number of Shots

More examples don’t always mean better performance. There’s a tradeoff: too few examples can lead to poor generalization, while too many can exhaust the token limit or introduce noise. In practice, 2–5 high-quality examples often yield strong results.

Pro tip: If you notice performance degrading after a certain number of examples, trim it back. Less can be more.

4. Diversity vs. Consistency

Examples should ideally cover slight variations of the task to help the model generalize—but they shouldn’t contradict each other in format, tone, or objective.

Balance: Include slightly different examples but maintain consistency in structure.

5. Input/Output Separation

Make it easy for the model to distinguish between the input and the desired output. Clear separators like Input: and Output:, or formatting (e.g., newlines, indentation), can significantly improve accuracy.

Example Format:

Input: What's the capital of France?
Output: Paris

Input: Who wrote Hamlet?
Output: William Shakespeare

Input: What’s the square root of 64?
Output:

6. Instructional Context Still Matters

Even in few-shot setups, adding an initial instruction or system message can help set the tone and expectations. Combine few-shot examples with a short instruction to reinforce what the model should do.

E.g.: “Answer all questions concisely and accurately.”

7. Order of Examples

Surprisingly, the order of your few-shot examples can impact performance. Placing the most relevant or “easiest to generalize from” examples first sometimes yields better results.

8. Test Across Variations

What works for one type of input might not generalize across the board. Test your few-shot prompt on a variety of test cases—especially edge cases—to make sure it holds up.

9. Token Limit Awareness

Few-shot examples consume tokens. If you’re working with long inputs (e.g., full documents, transcripts), you’ll need to strike a balance between including examples and allowing enough space for your actual input and expected output.

10. Tool-Assisted Prompt Design

If you’re building products on top of LLMs, consider using prompt engineering tools or frameworks to template and test your few-shot prompts systematically. This also helps scale and version-control your prompt logic.

Final Thoughts

Few-shot prompting is like giving the model a few pages from the instruction manual—it can learn fast, but only if your "pages" are well-written and to the point. The better you craft your examples and structure your prompt, the better your outputs will be.

Key Considerations When Using Few-Shot Prompting in LLMs