Wednesday, June 07, 2023
Maximizing your effectiveness with AI is about deepening your understanding of how to communicate with these advanced systems. Large Language Models (LLMs) have impressive zero-shot capabilities but can sometimes stumble on more intricate tasks. This is where "few-shot" prompting shines, adding another dimension to your AI journey.
In a nutshell, few-shot prompting involves providing the model with example prompts and desired responses to guide its understanding and enhance its performance for subsequent tasks.
Here's a common situation encountered by businesses that use or are considering using AI chatbots for customer interactions:
Example: Imagine you have an AI customer service chatbot, and you want to train it to respond to a specific type of question, such as, "Can I return a product after 30 days?" By supplying the model with a couple of demonstrations like:
Prompt: "I want to return my product after 45 days, is it possible?"
Output: "Our return policy allows returns up to 30 days from the date of purchase."
And ...
Prompt: "What is the maximum duration for returning a purchased item?"
Output: "You can return the item within 30 days of purchase."
And then presenting the model with the question: "Can I return a product after 30 days?"
The model should now correctly respond with something along the lines of: "Our policy allows for returns within 30 days of purchase, so a return after 30 days would not be possible."
You can increase the number of demonstrations to handle more challenging tasks. But, remember, the format used for demonstrations can significantly influence performance.
Now, few-shot prompting has its limitations. Consider a more complex customer interaction where a customer has a multifaceted billing issue involving multiple transactions and discounts. Despite providing several examples, the model might produce only partially accurate or satisfactory responses. This is because few-shot prompting might not be the correct kind of prompt for tasks needing deeper understanding or complex reasoning.
That's where advanced techniques such as chain-of-thought (CoT) prompting are being refined to tackle more complex tasks.
While providing examples can benefit certain tasks, zero-shot and few-shot prompting may not consistently deliver the desired results. Consider fine-tuning your models or experimenting with more advanced prompting techniques when the output isn't what you're after.
Next up in our series, we'll dive into the increasingly popular prompting technique, "chain-of-thought" prompting. Let's continue our thrilling journey into the heart of AI together!
