Few Shot Prompting

Few Shot Prompting is a powerful technique in AI and prompt engineering where a small number of examples are included in the prompt to guide a language model in performing a specific task. Unlike zero-shot prompting, where no examples are given, few shot prompting shows the model what kind of input-output behavior is expected by providing 2 to 5 relevant examples. This helps the model better understand the context and produce more accurate and relevant results.
Few Shot Prompting is especially useful when you want to achieve higher accuracy without retraining the model or having a large labeled dataset. It can be applied in various practical situations like text classification, translation, question answering, summarization, or customer support automation. By using carefully selected examples, you can shape the model’s responses to match your specific use case.
In this tutorial, you will learn how to create effective few shot prompts, how to select and format examples, and how to troubleshoot and improve your prompts. You will see practical examples that you can immediately use in your work, enabling you to harness the full power of modern language models in real-world applications.

This basic prompt clearly defines the task: converting Celsius temperatures to Fahrenheit. It begins by setting the role of the model as a "helpful assistant" to provide a guiding context. Then, two concrete examples are provided that demonstrate the input-output relationship—how Celsius values map to Fahrenheit.
By providing these examples, the model can infer the conversion formula implicitly, rather than guessing what is expected. Finally, the prompt asks the model to perform the conversion on a new input (25°C). This structure helps reduce ambiguity and guides the model to generate a correct and relevant output.
In practical scenarios, you can adjust the examples to fit different numerical conversions, formatting styles, or even other task types like sentiment analysis or text classification. Increasing the number of examples or varying their complexity can improve the accuracy but be mindful of prompt length limits.

This practical example builds upon the basic concept by showing how few shot prompting can be used to guide conversational AI. The prompt first sets the model as an expert customer support chatbot, establishing a clear role and tone.
It provides three diverse customer questions with well-crafted, polite responses that serve as templates for the model to emulate. The examples cover different types of common queries—password reset, order modification, and shipping details—demonstrating variety and consistency.
Finally, the prompt asks the model to generate a suitable response to a new customer query about order tracking. This kind of few shot prompt can be used directly in customer service bots to improve response relevance and professionalism.
Variations may include adjusting response styles (formal, casual), adding more examples for niche queries, or including additional context like product details to tailor answers further.

Best practices and common mistakes
Best practices:

1. Choose clear, representative examples that cover typical use cases of your task.
2. Keep example formats consistent to help the model recognize input-output patterns.
3. Use between 2 to 5 examples; too few may confuse the model, too many can exceed token limits.

4. Explicitly define the model’s role and the task context at the start of the prompt.
   Common mistakes:

5. Providing ambiguous or poorly formatted examples that confuse the model.

6. Mixing different styles or inconsistent example structures.
7. Including too many examples, causing the prompt to become too long or unfocused.
8. Forgetting to specify the model’s role or task, leading to generic or irrelevant outputs.
   Troubleshooting tips:

• If outputs are off-target, try simplifying or clarifying your examples.
• Test different numbers and types of examples to find the optimal balance.
• Maintain a clean, logical structure with clear delimiters between examples.
• Review generated responses and iterate on example wording to better guide the model.
  Iterating on your few shot prompts by experimenting and refining examples is key to unlocking better performance over time.

Advanced techniques and next steps
Few Shot Prompting can be enhanced by combining it with advanced methods like Chain-of-Thought prompting, which guides the model through step-by-step reasoning, improving complex task performance. It also integrates well with zero-shot prompting to handle cases where no examples exist, and with fine-tuning for more customized behavior.
Exploring multi-modal few shot prompting, where text inputs are combined with images or other data types, is another frontier for complex AI applications. As you progress, consider studying prompt optimization techniques and automated prompt generation tools to streamline your workflow.
Mastering Few Shot Prompting requires practice and experimentation across diverse tasks. Next, dive deeper into related topics such as prompt engineering frameworks, interactive prompt tuning, and reinforcement learning with human feedback (RLHF) to further enhance AI outputs.