Tipos de ChatTemplates: Few-Shot Prompting

Fused-Shot Prompting is a technique that allows guiding the language model by providing specific examples within the same prompt. This improves the accuracy of responses by showing the model how it should behave through clear examples. It is useful when we want the model to learn patterns from the instructions and examples we provide.

1. Types of Fused-Shot Prompting

There are two main ways to implement this technique:

• Fixed examples: here we establish a static set of examples that will be used in all interactions. These examples do not change and serve as a reference for the model to understand how to respond to certain types of questions or inputs.
• Dynamic examples: In this mode, examples are selected dynamically based on the semantic similarity between the user's input and the stored examples. This means that the system chooses the most relevant examples in real time, based on what the user asks.

Both ways help the model improve its answers by providing direct guidance on how to proceed in different situations.

2. Improving Answers with Concrete Examples

Providing clear examples within the prompt helps the model more accurately interpret ambiguous situations. For example, if we want the model to understand the use of emojis in mathematical operations, we can teach it with examples such as:

• Input: "How much is 🦜+🦜?"
• Expected output: "2"

These examples will help the model to interpret the emoji as a numerical value, instead of trying to infer an abstract or symbolic meaning.

3. Advantages of Fused-Shot Prompting

Fused-Shot Prompting allows:

• Guide the model clearly: By including explicit examples of the relationship between input and output, the model has a clear reference for how it should respond.
• Customize the interaction: You can adjust the examples according to the type of interaction or context you need, allowing you to adapt the model's behavior for specific cases.
• Improve accuracy in specific tasks: This approach is particularly useful in tasks where the model must follow specific rules, such as calculations, translations or symbolic interpretations.

4. Integration of Examples with Prompt Templates

To implement Fused-Shot Prompting in LangChain, you can use prompt templates that accept both static and dynamic examples. Instead of simply providing an instruction, you can include several examples in the same template, showing the model how it should process similar inputs.

The model processes these examples and learns the expected pattern, which increases the accuracy of responses for similar input that the user may provide in the future.

5. Structure of a Dynamic Prompt

When working with dynamic examples, the model selects the most appropriate examples based on similarity to the user's input. This is useful in scenarios where there are a wide variety of possible inputs, and you want the model to select the most relevant examples to generate its response.

Dynamic selection ensures that the examples that are semantically closest to the user's input are used as a reference, which maximizes the relevance of the response.

6. Using Chains to Integrate the Prompt and the Model

In LangChain, you can integrate the prompt with the model usingchains. Once you have configured the examples with Fused-Shot Prompting, you can combine the prompt and the model into a chain, allowing both to be invoked together. This facilitates continuous interaction without the need to repeat the model or prompt configuration for each query.

Chains allow you to chain the behavior of the model and prompt in a logical sequence, making it easier to query the model efficiently while maintaining the context of the conversation and the examples provided.

7. Conclusion

Fused-Shot Prompting is a powerful technique for teaching the model to respond more accurately and adapted to specific contexts. By providing examples within the prompt, we can guide the model's behavior, improve the relevance of responses, and make it better interpret user input. By using fixed or dynamic examples, and combining them with tools such as chains, it is possible to build more sophisticated applications, such as conversational agents, virtual assistants or recommender systems, that provide more accurate and consistent responses.

This technique is especially useful when you want the model to follow predefined patterns or when you need it to correctly interpret less conventional elements such as emojis, symbols or ambiguous situations.