Few Shot Prompting | Master Prompt Engineering | Learning Space Tutorials

Key Takeaways

[Music] welcome to this video now in this lesson we are going to learn about few short prompting now we have been learning about a lot of techniques and few short prompting is one of the techniques of prompting where an AI model learns to perform a task with just a handful of examples this is super useful when you have limited data few short prompting helps the model understand the task through contextual learning by presenting examples within the prompt let's start with a few examples to guide the model and if the output isn't perfect you can tweak the prompts or add more examples to improve accuracy let's see how this works with some awesome examples one short prompts one short prompting involves providing a single example along with the input data let's see it in action here 2 + 2 is equal to 4 is the example 4 + 4 is the input data for which we need a response so we have told the AI model to give us a response based on an example let's look further few short prompting lets you control the model better let's see how we can generate sentences for madeup words blur bbls are mythical creatures that live in enchanted forests an example of a sentence using the word blobbles is the blobbles danced under the Moonlight their shimmering Wings lighting up the forest now we created another magical word snor is a magical tool used to find the hidden treasures an example of a sentence using the word snor is the snor glowed brightly guiding the adventurer to the hidden treasure behind buried beneath the ancient oak tree now what we try to do here is that we let the model generate a sentence for another madeup word snor next we can also classify customer reviews into positive or negative sentiments using few short prompting over here I'm giving a pattern of the reviews of product movie food service and concert now you can see that I am not giving any s of sentiment at the end whether the review experience is negative or positive I leave up to the model to decide based on the input that I am providing now you can see that my output says that it's a positive review because the concept was absolutely mind-blowing so by providing examples we guide the model to classify the input based on the sentiment of the examples provided while few short prompting is powerful it is not without its challenges so here are some limitations to keep in mind the model might struggle with new or unusual examples because it relies heavily on the provided examples it can introduce bicis if the examples are not representative few short prompting works best for simpler tasks for complex tasks requiring deep understanding or multi-step reasoning more examples or a different approach might be needed with limited examples the model might not explore a wide range of possibilities leading to less variety and creativity in responses few shot prompting might not fully grasp the context or nuances of the task resulting in less accurate or appropriate responses now F shot prompting is a fantastic technique for training AI models with limited examples it's perfect for quick adaptations and tasks with limited data however you have to be mindful of its limitations and ensure whether it's suitable for our specific task or not hope this video made sense I'm going to see you in the next lesson till then keep learning and of course keep practicing [Music]