GenAI: Advanced Prompting

Welcome to the third blog post on genAI topic. Today we are going to cover several advanced prompting techniques that might come handy when using LLMs.

We are moving from prompt fundamentals that we covered earlier to advanced prompting with the promise to maximise utility and efficiency of genAI models.

Why are advanced techniques important?

Advanced prompt techniques significantly enhance the accuracy and relevance of AI responses, ensuring they meet specific user needs. They enable AI models to tackle complex problems through methods like chain-of-thought prompting, which breaks down tasks into manageable steps. Techniques such as few-shot and zero-shot learning allow models to adapt to new tasks with minimal data, offering efficient learning capabilities. These methods provide customization and flexibility, allowing users to tailor AI outputs for diverse applications.

Chain-of-thought (CoT) prompting

Let's start with a popular technique called Chain-of-Thought. It structures prompts to guide generative AI through a sequential reasoning process. This method breaks down complex problems into smaller, more manageable steps, allowing the AI to tackle each part sequentially and transparently. It is particularly useful for tasks that require logical reasoning, such as solving math problems or making sense of multi-step questions. By making the AI's thought process visible, it not only enhances the model's problem-solving capabilities but also allows users to understand and trust the AI's decision-making process better.

Tips how to build a CoT prompt with genAI models:

1. Start with a clear statement
2. Explicitly ask the AI to "show its work," "explain the reasoning," or "describe the steps"
3. Prompt the AI to consider different angles or possibilities and to explain why it chooses one over the other
4. End the prompt by asking for a summary or final conclusion

Example of the CoT prompt (using an example from my favorite sport: tennis, again):

Few-Shot Learning

Second prompting technique involves the user providing a small set of examples within the prompt to guide the model's understanding and response generation for a specific task. By including these examples, the user effectively teaches the model the desired pattern or format of the response, leveraging its ability to infer and generalize from limited data. This approach allows users to tailor the model's outputs to specific needs or formats without extensive retraining, making it a powerful tool for customizing AI responses with just a few carefully chosen examples.

Tips how to build a Few-shot-learning prompt with genAI models:

1. Choose your examples carefully to cover the breadth of the task or the variety of responses you're aiming for.
2. Ensure that the examples you provide are clear and consistent in format and style.
3. Tailor your examples to be as close to the actual use case as possible.
4. Be prepared to refine your examples based on the initial outputs you receive

Example of the Few-shot-learning prompt:

Zero-shot Learning

The last prompting technique we will dive into is Zero-shot learning. As a prompting technique it allows users to engage with a model on tasks it hasn't explicitly been trained for, without providing any task-specific examples. Users craft prompts that clearly define the task or question, relying on the model's pre-existing knowledge and generalization capabilities to generate a response. This approach is particularly useful for exploring a wide range of topics and tasks, leveraging the model's ability to infer and apply its training to novel scenarios.

While Few-shot Learning and Zero-shot Learning might seem opposite in terms of example usage, both techniques leverage the underlying model's ability to generalize from its training. Zero-shot learning tests the model's ability to apply its knowledge broadly without direct guidance, whereas few-shot learning aims to quickly specialize the model's responses with minimal examples.

Tips how to build a Zero-shot-learning prompt with genAI models:

1. Since zero-shot learning doesn't rely on examples, the clarity and specificity of your prompt are crucial. Clearly define the task, question, or problem in your prompt to guide the model towards the type of response you're seeking.
2. While you're not providing specific examples, including relevant context or background information within your prompt can significantly improve the model's response.
3. Zero-shot learning may not always provide the perfect response on the first try. Be prepared to refine your prompts based on the responses you get.

Example of the Zero-shot-learning prompt:

Advanced techniques compared in a table format:

Last but not least, it is important to mention that advanced prompting is only one of the 3 well known techniques that can be leveraged when trying to improve accuracy of responses.

1. Prompt Engineering: (we covered in this and previous blog post)
2. Retrieval Augmented Generation (RAG):RAG combines the capabilities of LLMs with external data retrieval to enhance the model's responses with up-to-date or specific information. During the prompting process, relevant data is fetched from a database or web endpoint and included in the prompt. This approach enriches the model's context, making its responses more informed and precise.
3. Fine-Tuning:Fine-tuning involves further training an LLM on a specific dataset to tailor its responses to particular needs or domains. This process makes the model more adept at handling tasks closely related to the fine-tuning data, significantly improving its performance on specialized tasks. However, fine-tuning requires additional computational resources and data, making it more costly than other methods.

We will be diving into the other 2 techniques in future blog posts. Stay tuned!

This content draws inspiration from existing materials and practices. As an employee of Microsoft, I want to clarify that the views and interpretations presented here are my own and do not necessarily represent the official policies or positions of Microsoft. This is intended for educational and informational purposes only.