Introduction
In this article, we'll explore the basic key concepts of Prompt Engineering, and we'll also explore techniques for developing prompts.
What is Prompt Engineering?
Creating input requests for Large Language Models (LLMs) specifically LLMs like GPT-3.5 and GPT-4, that will result in a desired outcome, Prompt Engineering can be considered as a form of art.
Basic Prompt
Basic prompts are sufficient to achieve a good output, but the amount and quality of the information offered determines the quality of the results.
Prompting Techniques
The techniques that will be covered in this article are-
1. Zero-Shot Prompting
In this zero-shot prompt, the model is asked to perform a task without being trained on a specific task. The model uses its understanding of language and context to generate a response. When zero-shot learning is not sufficient, it is advisable to include demonstrations or examples in the prompt (which leads to few-shot prompting).
2. One-Shot Prompting
In this one-shot prompt, the model is provided with just one example of a specific task. The expectation is that the model will generalize from this single example to perform similar translations.
3. Few-Shot Prompting
In this few-shot prompt, the model is given a couple of examples to help it understand the task. The provided examples serve as demonstrations, guiding the model on how to approach similar tasks. This technique is particularly useful when you have a specific task in mind and want the model to leverage its pre-trained knowledge in a targeted manner.
4. Chain of Thought (CoT)
The Chain of Thought technique refers to a method of interacting with language models where the user constructs a prompt in a way that encourages the model to generate a coherent and connected stream of responses. This technique is often used with models like GPT, which is designed to understand and generate human-like text based on the context provided in the prompt. In a Chain of Thought prompt, the user typically starts with an initial input or question and then uses the model's responses to guide the conversation coherently. The idea is to build on the context provided by the model's previous responses to create a continuous and logical flow of information or dialogue.
5. Self-consistency
Self-consistency prompt engineering is an advanced technique that aims to improve the quality and consistency of responses generated by large language models (LLMs), especially for tasks involving arithmetic and commonsense reasoning. It builds upon the concept of chain-of-thought (CoT) prompting, which involves breaking down a complex task into a series of smaller steps and asking the LLM to complete each step one by one.
6. Tree of Thoughts (ToT)
Tree-of-thoughts (ToT) prompt engineering is a technique for improving the reasoning ability of large language models (LLMs) by encouraging them to explicitly consider multiple possible explanations for a given problem. It is inspired by the human cognitive strategy of considering multiple hypotheses and evaluating their plausibility before concluding.
The ToT framework maintains a tree of thoughts, where each thought is a logical progression of words that acts as a step toward the solution of the problem. This method enables the language model to evaluate its own progress throughout a purposeful reasoning process using intermediate thoughts. Search methods such as breadth-first search and depth-first search are combined with the LM's ability to generate and evaluate thoughts to create ToT, which allows systematic exploration of concepts with lookahead and backtracking for effective problem resolution. The LM's ability to handle challenging tasks is improved by this more advanced method.
7. Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is a prompt engineering technique that combines information retrieval with text generation to improve the performance of large language models (LLMs) on knowledge-intensive tasks. It was first introduced by meta-researchers in 2020 and has since been shown to be effective on a variety of tasks, including question answering, summarization, and translation. RAG consists of two main components: a retrieval component and a generation component. The retrieval component searches a large corpus of text for documents that are relevant to the input query. The generation component then takes the input query and the retrieved documents as input and generates a response.
Conclusion
Prompt Engineering, an art form for achieving desired outcomes with Large Language Models like GPT-3.5 and GPT-4, involves creating input requests. While simple prompts can provide good outputs, the quality and quantity of information determine results. Techniques covered in this article are Zero-Shot, One-Shot, Few-Shot, Chain of Thought (CoT), Self-Consistency, and Tree of Thoughts (ToT). CoT guides coherent conversations, Self-Consistency improves response consistency, and ToT enhances reasoning by considering multiple explanations. Additionally, Retrieval Augmented Generation (RAG) combines information retrieval with text generation for knowledge-intensive tasks.