How You Can Use Few-Shot Learning In LLM Prompting To Improve Its Performance

You must’ve noticed that large language models can sometimes generate information that seems plausible but isn't factually accurate. Providing more explicit instructions and context is one of the key ways to reduce such LLM hallucinations.

That said, have you ever struggled to get an AI model to understand precisely what you want to achieve? Perhaps you've provided detailed instructions only to receive outputs that fall short of the mark? 

Here is where we’ll use the few-shot prompting technique to guide LLMs toward producing accurate, relevant, and properly formatted responses. In it, you’ll teach the LLM by example rather than through complex explanations. Excited?! Let’s begin! 

What Makes Few-Shot Learning So Powerful?

In the last year or so, LLM prompting has gone from simple text completion to more sophisticated instruction design. Understanding this evolution helps you appreciate where few-shot learning fits in the larger picture. 

At one end of the spectrum, we have simple completion prompts where the model continues a given text. At the other end, we have complex reasoning structures with multiple examples and explicit reasoning paths. Few-shot learning sits in the middle, providing just enough guidance through examples without the complexity of full reasoning chains.

Few-shot learning uses a fundamental aspect of how LLMs work: they're essentially pattern completion engines. When you show a model multiple examples that follow a consistent pattern, you're teaching it to recognize and continue that pattern with new inputs.

This approach mirrors how humans learn new tasks. When you're teaching someone a new skill, you rarely start with abstract rules. You demonstrate the task a few times and let them extract the pattern. Our brains are wired to learn from examples, and LLMs have been designed to mimic this cognitive process.

Refer to Figs 1 to 5 to see the striking similarities between human and LLM learning processes. 

How Does Few-Shot Prompting Work Intuitively?

From a technical perspective, few-shot learning works because of how LLMs use attention mechanisms to establish relationships between tokens in the prompt. When you provide examples, you're able to create a temporary pattern recognition framework within the model's context window. 

The self-attention mechanism in transformer-based models allows the LLM to identify relationships between your examples and the new query. As the model processes your prompt, it pays special attention to similarities between your demonstrated pattern and the new input you want it to process.

What's fascinating is that this happens without any gradient updates or fine-tuning. The model is learning "on the fly" within the context of a single prompt. This in-context learning capability is what makes few-shot learning so practical and accessible.

See Fig. 6 to visualize how attention patterns flow through a few-shot prompt.

Few-Shot Examples That Work Well

Creating effective few-shot prompts is both an art and a science. You’ll need to keep in mind that there's no universal formula that works for all situations. Sometimes, certain principles consistently lead to better results. Let's examine what makes examples truly effective.

Example Selection Principles

The examples you choose directly influence the model's outputs. Your goal is to select examples that clearly demonstrate the pattern you want the model to follow, while covering the range of variations the model might encounter.

Think of your examples as teaching samples. They should be representative, diverse, and clearly illustrate the task. Most of the time, selecting a bad example is one of the most common reasons for suboptimal few-shot performance.

Some key principles are: 

1. Diversity (Covering Different Cases)

• Do: Use legal document examples for a legal classification task
• Don’t: Use examples from a completely different domain that may introduce confusion

2. Representativeness (Matching Real-World Data)

• Do: For a medical symptom classifier, use actual patient descriptions rather than textbook definitions
• Don’t: Use artificially perfect or simplified examples that don't reflect real user inputs

3. Inclusion of Edge Cases

• Do: Include examples of subtle sarcasm or mixed sentiments that might be ambiguous
• Don’t: Only include obvious, clear-cut examples that don't challenge the model

4. Progressive Complexity

• Do: Starting with basic examples before showing examples with exceptions or special rules
• Don’t: Jump straight to the most complex cases without showing basic patterns

5. Contextual Relevance

• Do: Use legal document examples for a legal classification task
• Don’t: Use examples from a completely different domain that may introduce confusion

6. Balanced Quantity Across Categories

• Do: Give 2-3 examples for each possible classification category
• Don’t: Including 5 examples of one category but only 1 example of another

Structuring Your Few-Shot Prompts

The structure of your prompt is as important as the examples themselves. A well-structured few-shot prompt contains clear sections and consistent formatting that makes the pattern easy for the model to detect.

Your prompt should typically include:

• A clear task description that explains what you want
• Multiple examples with consistent formatting
• A clear separation between examples and your new query
• Explicit input/output indicators in each example

Look at Figs. 7, 8, 9, 10, and 11 below to understand this better.

 

The formatting should remain identical across all examples. If one example uses "Input:" and "Output:" as labels, all examples should use the same labels. Inconsistent formatting can confuse the model about which pattern to extract.

Advanced Few-Shot Techniques

As you become comfortable with the basic few-shot learning, you can explore more advanced techniques that further enhance your prompt's effectiveness. These approaches are especially valuable for complex tasks that require multi-step reasoning or high accuracy.

1. Hybrid Chain-of-Thought Few-Shot Learning

Standard few-shot learning works well for straightforward classification or transformation tasks, but struggles with problems that need complex reasoning. Chain-of-Thought (CoT) prompting addresses this limitation by demonstrating not just inputs and outputs, but the entire reasoning process.

By combining few-shot learning with chain-of-thought reasoning, you show the model how to "think through" a problem step by step. This is particularly effective for mathematical problems, logical reasoning, and any task where the path to the answer is as important as the answer itself.

Refer to Figs. 12 and 13 to see a comparison between standard few-shot prompting and chain-of-thought few-shot prompting. Notice how CoT examples reveal the entire reasoning process and can significantly improve performance on complex tasks.

2. Self-Consistency Few-Shot Learning

This advanced technique generates multiple independent responses through few-shot prompting and selects the most consistent answer. The method works by:

1. Sending the same few-shot prompt to the model numerous times with temperature > 0
2. Generating diverse reasoning paths to the solution
3. Extracting the final answer from each path
4. Selecting the most frequently occurring answer as the final result

This approach leverages the wisdom of crowds principle. If multiple reasoning attempts converge on the same answer, it's more likely to be correct. You’ll see this work wonderfully in Fig. 14.

Real-World Applications of Few-Shot Learning 

Few-shot learning has changed how we interact with LLMs across various domains. Let’s take a quick look at how this technique, when applied in practice, can spark ideas for your own implementations.

• Custom data extraction: Extract structured information from unstructured text in your specific format, perfect for processing reviews, support tickets, or documents
• Consistent style generation: Generate content that matches your brand voice or editorial style across different writers
• Custom classification: Classify text according to your unique categories and criteria, ideal for routing customer inquiries or content moderation
• Standardized data transformation: Convert data between formats with your specific transformation rules
• Specialized knowledge QA: Create a domain-specific question answering system with your proprietary knowledge

As you’ll see below (See Fig 15), you can use LLMs to do complex data migration, integration, and cleaning tasks without writing complex rule-based systems, which eventually fail for new formats or are too rigid.

What To Do If Few-Shot Doesn’t Work?

Sure, few-shot prompting can work wonders, but it isn't without challenges. When your few-shot prompts aren't performing as expected, you can refer to the list below: 

• Low accuracy or inconsistent results: Often caused by insufficient example diversity or poor example selection
• Format inconsistency: The model doesn't follow your expected output format because your examples don't establish a clear pattern
• Context length limitations: Your prompt becomes too long with many examples, hitting the model's context limit
• Overfitting to example details: The model reproduces specific phrases from examples rather than understanding the underlying pattern

Each of these issues has specific solutions that can dramatically improve your results. For instance, if the LLM: 

• Provides correct answers occasionally but fails regularly
• Gives outputs that vary widely with slight changes in input format

Then you can: 

• Increase example diversity to cover more edge cases
• Ensure examples represent the full range of expected inputs
• Try using more examples (5-8 instead of 2-3)
• Check if your examples contain subtle patterns that the model might misinterpret

Here’s something for you to think about. What would you do if the LLM: 

• Truncates responses or stops generating mid-output
• Gives worse output when you add more examples
• Throws error messages about exceeding the maximum context length

Conclusion

We’ve seen how few-shot learning can be a great way to guide LLMs without specialized training. Throughout this article, we've explored the fundamentals, advanced techniques, and seen some real-world applications. 

To master this technique, remember these key principles:

1. Start simple, then refine: Begin with 2-3 diverse examples and clear instructions. Test performance and gradually refine based on results.
2. Quality over quantity: Remember that well-chosen, representative examples will consistently outperform larger sets of lower-quality examples.
3. Format consistently: Maintain identical formatting across all examples.
4. Consider advanced techniques: For complex tasks, explore chain-of-thought or self-consistency approaches.
5. Document what works: Take time to build a library of your own few-shot patterns for different tasks.

By showing models what you want through carefully selected examples, you're developing a more natural way to interact with AI systems!