Prompt Engineering for Beginners

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Introduction

In this article, you will learn the basics of prompt engineering. Then you will get to know about What is prompt engineering, why it is important, and various techniques to master this skill. Additionally, you will learn what are the examples of effective prompts for different tasks which will help you to jumpstart your journey into prompt engineering.

What is Prompt Engineering

The main focus of prompt engineering is to enhance the interaction between humans and Artificial Intelligence. We can term prompt engineering as the process of creating and constructing prompts to lead a large language model to produce the required output. These LLMs are trained on a large number of datasets containing text and code. While using LLMs one must know that they can be difficult to use effectively without careful prompting. By offering users with precise instructions, relevant context, and useful examples, prompt engineering can assist them in getting the most out of large language models.

Importance of Prompt Engineering

Prompt engineering is important for making large language models work better. The following are the importance of prompt engineering:

• Prompt engineering can help us to shorten the effort and time required to generate LLM output.
• Give more accurate and instructive responses from LLMs.
• Enhance the quality and creativity of LLM output.
• Enlarge the range of tasks that are to be performed using LLMs.

Example:

Suppose you want to use an LLM to generate an article about the Eiffel Tower in Paris. You can simply provide the LLM with a prompt that says "Write an article about the Eiffel Tower in Paris." However, this is likely to result in an article that is poorly written, inaccurate, or irrelevant.

Instead, you can use prompt engineering to guide the LLM to generate a more accurate and informative article. For example, you can provide the LLM with a prompt that specifies the following:

 The topic of the article: "The Eiffel Tower: A Symbol of Paris"

 The desired length of the article: "500 words"

 The audience for the article: "General readers"

 Tone and style of the article: "Informative and engaging"

 The following diagram illustrates how prompt engineering can be used to improve the quality of LLM output:

                                                                                                                      (Diagram No. 1)

The above diagram shows how a prompt can be used to guide the LLM to generate a more accurate and informative news article about Eiffel Tower in Paris.

Different Kinds of Prompt Engineering Techniques

There are a variety of prompt engineering techniques that can be used to achieve different goals. Some common techniques include:

• Zero-shot prompting: It provides the LLM with a prompt that describes the desired output, without providing any examples. Zero-shot prompting is often used for tasks such as summarization, translation, and question-answering.
• Few-shot prompting: It provides the LLM with a few examples of the desired output, along with a prompt that describes the task. Few-shot prompting is often used for tasks such as creative writing and code generation etc.
• In-context prompting: It provides the LLM with a sequence of prompts, where each prompt builds on the previous one. In-context prompting is often used for tasks such as reasoning, problem-solving, and dialogue.

Examples of Effective Prompts for Different Tasks:

The few examples below illustrate how you can use well-crafted prompts to perform different types of tasks.

Tasks:

• Text Summarization
• Question Answering
• Text Classification
• Code Generation
• Information Extraction
• Conversation
• Reasoning

                                                                                                                         (Diagram No. 2)

Text Summarization

It is the ability to summarize articles and concepts into quick and easy-to-read summaries. Let’s have a look on a basic summarization task using prompts.

Let's say you are interested in learning about Blockchain Technology, you can try a prompt like this:

Prompt:

Here, you used it to inform the model that a subsequent response was expected. Let's imagine that you feel that this material is too much and that you would like a more thorough summary. In fact, you may tell the model to compress everything into a single sentence:

Prompt:

Here you can see the model tried to summarize the whole paragraph in one sentence.

Question Answering

One of the best ways to get the model to respond is to improve the format of the prompt. Basically, a prompt can combine given input , output ,instructions, context indicators to get enhanced results.

Here are some examples of how QA systems can be used to answer the questions you provided.

Prompt:

A QA system would be able to answer this question by searching its knowledge base for information about the chemical formula of water. It would then generate a response that includes the answer to the question.

Text Classification

It is the process of designing prompts that guide language models to perform text classification tasks. This can be achieved by providing the model with specific instructions and context, as well as examples of the different kinds of classes for text, so that it will be able to identify.

For example, the following prompt could be used to classify customer reviews as neutral, positive or negative:

Prompt:

The tech industry continues to revolutionize our world with cutting-edge innovations that redefine the way we live and work.

Code Generation

It is the process of designing prompts that guide language models for generating code. This can be achieved by providing the model with specific instructions and context, as well as examples of the desired code output.

For example, the following prompt could be used to generate a code that takes a string as input and returns the reverse of the string by using Python.

Prompt:

Information Extraction

It is the process of designing prompts that guide language models to extract specific information from text. This can be achieved by providing the model with instructions and context, as well as examples.

For example, the following prompt can be used to extract the names of all people mentioned in the news article:

Prompt:

Conversation:

It is a technique used to create more natural and engaging conversations between humans and AI language models. Conversation provides the model with context and information about the user's goals, and then it asks questions in such a way that encourages the model to respond in a conversational manner.

Now, we will create a conversational system that can answer queries in a more technical and perfect manner, for example. Keep in mind that by giving it instructions, you are clearly directing it how it must act. This is sometimes referred to as role prompting.

 Prompt:

Reasoning:

It involves designing prompts that will encourage language models to use their knowledge and reasoning skills to generate accurate and informative responses. This can be achieved by providing the model with clear feedback, instructions and examples.

Prompt-1:

 Prompt-2:

As we can see in the above example, the model is first asked to add 98 and 2. Then, it is asked to subtract 4 from 8. Finally, it is asked to multiply 6 by 4. By performing these steps one at a time, the model is able to solve the problem correctly.

In each of these examples, the prompt is carefully designed to guide the LLM to generate the desired output. By following these examples, you can start to use prompt engineering to get more out of LLMs.

Conclusion

In this article, we have explored the basics of prompt engineering, including its definition, importance, and different techniques. We have also provided examples of effective prompts for different tasks, such as text summarization, question answering, text classification, code generation, information extraction, conversation, and reasoning. As we all know that Prompt engineering is a rapidly evolving field, that can be used to generate a wide variety of outputs from LLMs. As LLMs become more powerful and versatile, prompt engineering will surely play an increasingly important role in how we interact with AI.

Author Bio

Sangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.