Prompt Engineering Techniques - Explained | Artificial Intelligence Interview Questions & Answers

Key Takeaways

in this question we'll look at uh four different types of prompt engineering uh which are zero short prompting few short prompting Chain of Thought prompting and finally self-consistency prompting uh let's discuss them one by one uh first is zero short prompting it is a prompting technique that involves generating text without any training on the specific task at hand instead the generative AI system uses a pre-exist in language model trained on diverse tasks to generate text for a new task without additional training or fine-tuning and if you think about it this has a huge benefit Jero short prompting allows for quick and easy adaptation to new task without requiring large amounts of task specific training data this is what it looks like the next is f short prompting few short prompting is a technique in which a model is trained to perform a specific task with limit limited or few training example hence the name few short prompting in few short learning the model is uh F tuned on a smaller data set of examples often referred to as a short this is done to learn the underlying patterns and rules of the task the model is then tested on a separate data set called the query set to evaluate its performance few short prompting can be helpful when the data available for training is limited or costly to obtain this is what few short prompting looks like the next is Chain of Thought prompting or coot prompting this allows the model to achieve uh complex reasoning through middle reasoning steps what do I mean by that at its core Chain of Thought prompting is about guiding the llm to think step by step rather than just giving a direct output this is achieved by providing the model with a few short examples that outline the reasoning process the model is then expected to follow a similar chain of thoughts while answering the prompt this is what a Chain of Thought prompting looks like all right finally we have self-consistency prompting this basically Builds on Chain of Thought prompting self-consistency aims to replace the naive greedy decoding uh used in Chain of Thought prompting uh just imagine you are trying to write a story one sentence at a time and each sentence is based on the previous one that you may have written the way you are doing it right now is to write the next sentence without really thinking too much about how it fixed with the previous one that's what we mean by naive greedy decoding now self-consistency is like a smarter way to do this it means that when you write each new sentence you are paying more attention to make sure it fits well with what you have written before it's like checking if your new sentence makes sense and matches the story's tone and style self-consistency involves providing the AI model with multiple reasoning parts or diverse perspectives and then selecting the most consistent and coherent answer among the generated responses this technique not only helps to reduce biases in the AI responses but also encourages it to consider various viewpoints before arriving at the conclusion so that's all about uh types of prompting uh if you guys have any doubts till now let us know in the comment section below and we'll get back to you