This blog is a comprehensive guide to understanding and utilizing different prompting techniques used to generate high quality responses from ChatGPT. We will explore how different prompting techniques can be used to achieve different goals. ChatGPT is a state of the art language model that is capable of generating human-like text. However, it is important to understand the correct way to ask ChatGPT in order to get the high quality results we desire
Whether you are a regular person, a researcher, a developer or just someone who wants to use ChatGPT as your personal assistant in your field, this blog is written for you. I have used simple language with to-the-point practical explanations along with examples and prompt formulas for each prompting technique.
In this blog, you will learn how you can use prompt engineering techniques to control the output of ChatGPT and generate text that is customised to your specific needs. In this blog you will also find examples of how you can combine different prompting techniques to achieve even more specific results.
What is prompt engineering?
Prompt engineering is the process of creating prompts, questions or instructions that control the output of a language model such as ChatGPT. It allows users to control the output of the model and generate text tailored to their specific needs.ChatGPT is a state-of-the-art language model capable of generating human-like text.It is based on the Transformer architecture, which makes it possible to process large amounts of data and generate high-quality text. However, to achieve the best results with ChatGPT, it is important to understand how to guide the model correctly.
This is where prompt engineering comes play. By giving clear and specific instructions, you can direct the output of the model and ensure that it is relevant.A prompt formula is a specific format for the prompt and usually consists of three main elements: Task: a clear and concise statement of what you want the model to generate with the prompt.Instructions: the instructions you want the model to follow when generating text.Role: the role you want the model to take when generating text.
“The prompting technique is a way to control the output of ChatGPT by giving the model specific instructions to follow. This technique is useful to ensure that the output is relevant and of high quality.To use the prompting technique, you need to give the model a clear and precise task and specific instructions to follow. For example, if you are generating responses to customer enquiries, you would provide a task such as “Generate responses to customer enquiries” and instructions such as “Responses should be professional and contain accurate information”.prompt formula: “Generate [task]following these instructions:[instructions]”
Examples:Generate responses to customer enquiries:
Task:Generate responses to customer enquiries
Instructions:Responses should be professional and provide accurate information
Request formula: “Generate professional and accurate responses to customer enquiries by following these instructions:
Responses should be professional and provide accurate information.
“Generate a legal document: Task:
Generate a legal documentInstructions:The document should comply with relevant laws and regulationsRequest wording: “Generate a legal document that complies with relevant laws and regulations by following these instructions
instructions:The document should comply with the relevant laws and regulations. “When using the instruction prompting technique, it is important that the instructions are clear and specific to ensure that the output is relevant and of high quality.The instruction prompting technique can be combined with “role prompting” and “seed word prompting”, as explained in the next chapter, to improve the output of ChatGPT.
Role promptingThe role prompting technique is a way to control the output ofChatGPT by assigning a specific role to the model.This technique is useful for generating text that is tailored to a specific context or audience.To use the role prompting technique, you must assign a clear and specific role for the model to assume.For example, if you are generating responses to customer enquiries, specify a role such as “Customer Service Representative”.Callout formula: “Generate [task]as [role]”Example:Generate responses to customer enquiries:Task:Generate responses to customer enquiriesRole:Customer service representativeCallout formula: “Generate responses to customer enquiries as a customer service representative. “Example:Generate a legal document:Task:Generate a legal documentRole:LawyerCallout formula: “Generate a legal document as a lawyer. “Using the role prompting technique with instruction prompting and keyword prompting will improve the output of ChatGPT.Here is an example of how the instruction prompting,role prompting and seed word prompting techniques can be combined:Task:Create a product description for a new smartphoneInstructions:The description should be informative and persuasive, highlighting the unique features of the smartphoneRole:Marketing representativeSeed word: “innovative”
Prompt: “As a marketing representative, create an informative, persuasive product description that highlights the innovative features of the new smartphone.The smartphone has the following features [enter your features].
”In this example, the prompt with the instruction is used to ensure that the product description is informative and persuasive. The prompt with the role is used to ensure that the description is written from the perspective of a marketing representative. And the prompt with the seed word is used to ensure that the description focuses on the innovative features of the smartphone.
Standard prompts
Standard prompts are an easy way to control the output of ChatGPT by giving the model a specific task. For example, if you want to create a summary of a news article, give a task such as “Summarise this news article”.
Prompt formula: “Create a [task]”
Example:Create a summary of a news article:
Task:Summarise this news article
Prompt formula: “Create a summary of this news article “Create a product review:
Task:Write a review of a new smartphone
Prompt formula: “Create a review of this new smartphone
“In addition, standard prompts can be combined with other techniques such as role prompting and seed word prompting to improve the output ofChatGPT.
Here is an example of how the standard prompts, role prompting and seed word prompting techniques can be combined:
Task:Create a product review for a new laptop Instructions:
The review should be objective and informative, highlighting the unique features of the laptop
Role:Technical expert
Keyword: “powerful “Prompt formula: “As a technical expert, create an objective and informative product review that highlights the powerful features of the new laptop.”
In this example, the standard prompting technique is used to ensure that the model creates a product report. The role prompting is used to ensure that the report is written from the perspective of a technical expert. And the start word prompting is used to ensure that the report focuses on the powerful features of the laptop.
Zero-, One- and Few-Shot Prompting
Zero-shot, one-shot and few-shot prompting are techniques used to generate text from ChatGPT with minimal or no examples.These techniques are useful when there is limited data available for a particular task or when the task is new and not yet well defined.The zero-shot prompting technique is used when no examples are available for the task.The model is given a general task and it generates text based on its understanding of the task.The one-shot prompting technique is used when only one example is available for the task.The model is given the example and it generates text based on its understanding of the example.The “few-shot prompting” technique is used when only a limited number of examples are available for the task; the model is presented with the examples and generates the text based on its understanding of the examples.Prompt formula: “Generate text based on [number]examples “Example:Generate a product description for a new product for which no examples are available:Task:Write a product description for a new smartwatchPrompt formula: “Generate a product description for this new smartwatch with zero examples “Generate a product comparison with one example available:Task:Compare a new smartphone with the latest iPhonePrompt formula: “Generate a product comparison for this new smartphone with one example (latest iPhone) “Generate a product review with few examples available:
Task:Write a report on a new e-readerPrompt: “Write a report on this new e-reader with a few examples (3 other e-readers) “These techniques can be used to create a text based on an understanding of the model for the task or the examples given.
The “Let’s Think About It” Prompt
“The “Let us Think About It” prompt is a technique that encourages ChatGPT to produce text that is reflective and thoughtful.This technique is useful for tasks such as essay writing, poetry writing, or creative writing.The prompt for the “Let us Think About It” prompt is simply the phrase “Let us think about it” followed by a topic or question.Example:Write a reflective essay:Task:Write a reflective essay on personal growthPrompt phrase: “Let us think about:personal growth “Write a poem:Task:Write a poem about the changing seasonsPrompt phrase: “Let us think about:the changing seasons “This prompt invites a conversation or discussion about a particular topic or idea.The model receives a prompt that serves as a starting point for conversation or text generation. The model then uses its training data and algorithms to generate a response that is relevant to the prompt. This technique allows ChatGPT to generate a contextually appropriate and coherent text based on the prompt provided.To use the “Let’s think about this prompt” technique with ChatGPT, you can take the following steps:1. Formulate a prompt that clearly states the topic or idea and initiates the conversation or text generation.3. Precede the prompt with “Let us think about this” or “Let us discuss” to indicate that you are initiating a conversation or discussion.
Here are some examples of prompts that use this technique:Prompt: “Let us think about the impact of climate change on agriculture “Prompt: “Let us discuss the current state of artificial intelligence “Prompt: “Let us talk about the pros and cons of remote working “You can also add an open-ended question, statement or piece of text to build the model on.Once you have entered the prompt, the model uses its training data and algorithms to generate a response that is relevant to the prompt and continues the conversation in a coherent way.This unique prompt helps ChatGPT to provide responses from different perspectives and angles, resulting in more dynamic and informative passages.The steps to use the prompt are simple and easy to follow, and it can really make a difference in your writing.Try it out and see for yourself
Self-consistency prompt
The self-consistency prompt is a technique used to ensure that the output of ChatGPT matches the input.This technique is useful for tasks such as fact checking, data validation or consistency checking when creating text.The prompt formula for the self-consistency prompt is the input text followed by the statement “Please ensure that the following text is self-consistent “Alternatively, the model can be asked to generate text that matches the input.Examples of prompts and their formulas:Example 1:Text generationTask:Generate a product reviewInstructions:The review should match the product information from the inputPrompt formula: “Generate a product review that matches the following product information [insert product information]”Example 2:Text summaryTask:Summarise a news articleInstructions:The summary should match the information provided in the articlePrompt: “Summarise the following news article to match the information provided [insert news article]”Example 3:Text completionTask:Complete a sentenceInstructions:The completion should match the context provided in the input
Prompt: “Complete the following sentence so that it matches the context given [insert sentence]”Example 4:1.Fact check:Task:Check whether a given news article is consistentInput text: “The article says that the population of the city is 5 millionis5million, but later it says that the population is 7 million. “Prompt: “Please make sure that the following text is consistent:The article states that the population of the city is 5 millionis5million, but later it says that the population is 7 million.” 2.Data verification:Task:Check whether a given data set is consistentInput text: “The data shows that the average temperature in July is 30 degrees, but the minimum temperature is given as 20 degrees. “Prompt: “Please ensure that the following text is consistent:The data shows that the average temperature in July is 30 degrees, but the minimum temperature is given as 20 degrees.”
Seed word prompt
The seed word prompt is a technique that allows you to control the output ofChatGPT by giving it a specific seed word or phrase.The prompt formula for the seed word prompt is the seed word or phrase followed by the statement “Please generate text based on the following seed word “Examples:Text generation:
Task:Generate a story about a dragonSeed
word: “Dragon
“Prompt formula: “Please generate text based on the following seed word:Dragon
“Language translation:
Task:Translate a sentence from English to Spanish
Seed word: “Hello “Prompt formula: “Please generate text based on the following
Seed word:Hello “This technique allows the model to generate text related to the seed word and expand it.In this way, you can control that the text generated by the model refers to a specific topic or context. The prompt with the start word can be combined with the input of roles and instructions to create more specific and targeted text.by specifying a start word or phrase, the model can generate text that relates to that start word or phrase, and by specifying information about the desired output and role, the model can generate text in a particular style or tone that matches the role or instructions.This allows greater control over the generated text and can be useful for a variety of applicationsHere are prompt examples and their formula:Example 1:Text generation
Task:Generate a poemInstructions:The poem should refer to the base word “love” and be written in the style of a sonnet.Role:PoetPrompt formula: “As a poet, compose a sonnet that refers to the keyword ‘love’. “Example 2:Text completionTask:Complete a sentenceInstructions:The completion should refer to the keyword “science” and be written in the style of a research paperRole:ResearcherPrompt: “As a researcher, complete the following sentence in a way that refers to the keyword ‘science’ and is written in the style of a research paper: [insert sentence]”Example 3:Text summaryTask:Summarise a news articleInstructions:The summary should relate to the keyword “politics” and be written in a neutral and unbiased toneRole:JournalistPrompt: “As a journalist, summarise the following news article in a way that relates to the keyword ‘politics’, written in a neutral and unbiased tone: [insert news article]”
Chapter 9:Knowledge generation promptThe knowledge generation prompt is a technique used to elicit new and original information from ChatGPT.The prompt formula for the knowledge generation prompt is “Please generate new and original information about X”, where X is the topic of interest.This is a technique that uses a model’s existing knowledge to generate new information or answer a question.To use this prompt with ChatGPT, a question or topic should be provided to the model as input, along with a prompt that specifies the task or goal for the generated text.The prompt should include information about the desired output, such as the type of information to be generated.The prompt should contain information about the desired output, such as the type of text to be generated and any specific requirements or restrictions.Here are examples of prompts and their formulas:Example 1:Knowledge generationTask:Generate new information about a specific topicInstructions:The information generated should be accurate and relevant to the topicPrompt formula: “Generate new and accurate information about [specific topic]”Example 2:Question answeringTask:Answer a questionInstructions:The answer should be accurate and relevant to the questionPrompt formula: “Answer the following question: [insert question]”Example 3:Knowledge integrationTask:Integrate new information with existing knowledge
Instructions:The integration should be accurate and relevant to the topicPrompt phrase: “Integrate the following information with existing knowledge about [specific topic]: [insert new information]” Example 4:Data analysis:Task:Generate insights about customer behaviour from a given data setPrompt: “Please generate new and original information about customer behaviour from this data set”
Chapter 10:Knowledge integration promptsThis technique uses a model’s existing knowledge to integrate new information or connect different pieces of information.This technique is useful for combining existing knowledge with new information to develop a more comprehensive understanding of a particular topic.How to use it with ChatGPT:The model should receive a new piece of information and the existing knowledge as input, along with a prompt that specifies the task or goal for the generated text.The prompt should include information about the desired output, such as the type of text to be generated and any specific requirements or constraints.Examples of prompts and their formula:Example 1:Knowledge integrationTask:Integrate new information into the existing knowledgeInstructions:The integration should be accurate and relevant to the topicPrompt formula: “Integrate the following information into the existing knowledge about [specific topic]: [insert new information]”Example 2:Connecting informationTask:Connect different pieces of informationInstructions:The connections should be relevant and logicalPrompt: “Connect the following information in a way that is relevant and logical: [insert information 1] [insert information 2]”Example 3:Updating existing knowledgeTask:Update existing knowledge with new information
Instructions:The updated information should be accurate and relevantPrompt: “Update existing knowledge about [specific topic] with the following information: [insert new information]”
Chapter 11:Multiple choice promptsThis technique presents a model with a question or task and a set of given options as possible answers. This technique is useful for generating text that is limited to a certain number of options, and can be used for answering questions, completing text, and other tasks.To use the multiple choice prompt with ChatGPT, the model should receive a question or task as input, along with a set of predefined options as possible answers.The prompt should also contain information about the desired output, such as the type of text to be generated and any specific requirements or constraints.Examples of prompts and their formula:Example 1:Answer a questionTask:Answer a multiple-choice questionInstructions:The answer should be one of the given optionsPrompt: “Answer the following question by selecting one of the following options: [insert question] [insert option 1][insert option 2] [insert option 3]”Example 2:Text completionTask:Complete a sentence using one of the given optionsInstructions:The completion should be one of the given optionsPrompt: “Complete the following sentence by selecting one of the following options: [insert sentence] [insert option 1] [insert option 2] [insert option 3]”Example 3:Sentiment analysisTask:Classify a text as positive, neutral or negative
Instructions:The classification should be one of the given optionsPrompt: “Classify the following text as positive, neutral or negative by choosing one of the following options: [insert text][positive] [neutral] [negative]”
Chapter 12:Interpretable soft promptsInterpretable soft prompts are a technique that allows you to control the text generated by the model while giving the model some flexibility.This technique allows for more interpretable and controllable generated text.Prompt examples and their formula:Example 1:Text generationTask:Generate a storyInstructions:The story should be based on a certain number of characters and a certain topicPrompt formula: “Generate a story based on the following characters: [insert character]and the topic: [insert topic]”Example 2:Text completionTask:Complete a sentenceInstructions:The completion should be in the style of a specific authorPrompt: “Complete the following sentence in the style of [specific author]: [insert sentence]”Example 3:Language modellingTask:Produce text in a particular styleInstructions:The text should be in the style of a particular periodPrompt: “Produce text in the style of [insert particular period]:[insert context]”
Prompt phrase: “Generate a professional and accurate dialogue for a customer service chatbot when the customer asks for [insert topic]”So this technique is useful for tasks such as dialogue generation, story writing and chatbot development.
Chapter 17:Adversarial promptsAdversarial prompts is a technique that allows a model to generate text that is resistant to certain types of attacks or distortions.This technique can be used to train models that are more robust and resistant to certain types of attacks or distortions.To use adversarial prompts with ChatGPT, you should provide the model with a prompt that is designed to make it difficult for the model to generate text that matches the desired output.Examples of prompts and their formulas:Example 1:Adversarial prompt for text classificationTask:Generate a text that is classified as a specific labelInstructions:The generated text should be difficult to classify as a specific labelPrompt formula: “Generate a text that is difficult to classify as [insert label]”Example 2:Adversarial prompt for sentiment analysisTask:Generate a text that is difficult to classify as a specific sentimentInstructions:The generated text should be difficult to classify as a specific sentimentPrompt formula: “Generate a text that is difficult to classify as a sentiment of [insert sentiment]”Example 3:Adversarial prompt for language translationTask:Generate a text that is difficult to translateInstructions:The generated text should be difficult to translate into the target language
Prompt formula: “Generate a text that is difficult to translate into [insert target language]”
Chapter 18:Clustering PromptsClustering prompts is a technique that allows a model to group similar data points based on certain features or properties.This is achieved by providing the model with a set of data points and asking it to group them into clusters based on certain features or properties.This technique is useful for tasks such as data analysis,machine learning and natural language processing.
How to use it with ChatGPT: The model should be given a set of data points and asked to group them into clusters based on certain features or properties. The prompt should also include information about the desired output, such as the number of clusters to generate and any specific requirements or constraints.Examples of prompts and their formulas:Example 1:Clustering customer reviewsTask:Group similar customer reviews togetherInstructions:The reviews should be grouped based on sentimentPrompt formula: “Group the following customer reviews into clusters based on sentiment: [insert reviews]”Example 2:Clustering news articlesTask:Group similar news articles togetherInstructions:The articles should be grouped together based on topicPrompt: “Group the following news articles into clusters based on topic: [insert article]”Example 3:Clustering of scientific articlesTask:Cluster similar scientific articles togetherInstructions:The articles should be grouped together based on research areaPrompt: “Cluster the following scientific articles into clusters based on research area: [insert papers]”
Chapter 19:Reinforcement learning promptsReinforcement learning prompts are a technique that allows a model to learn from its past actions and improve its performance over time.To use reinforcement learning prompts with ChatGPT, a set of inputs and rewards should be provided to the model so that it can adjust its behaviour based on the rewards it receives.The prompts should also include information about the desired outcome, such as the task to be completed and any special requirements or constraints. This technique is useful for tasks such as decision making,games and natural language generation.Examples of prompts and their formulas:Example 1:Reinforcement learning for text generationTask:Generate text that matches a particular styleInstructions:The model should adjust its behaviour based on the rewards it receives for generating text that matches the particular stylePrompt formula: “Use reinforcement learning to generate text that matches the following style [insert style]”Example 2:Reinforcement learning for language translationTask:Translate text from one language to anotherInstructions:Tell the model to adapt its behaviour to the rewards it receives for correct translationsPrompt: “Use reinforcement learning to translate the following text [insert text]from [insert language]to [insert language]”Example 3:Reinforcement learning for question answeringTask:Generate an answer to a questionInstructions:Tell the model to adapt its behaviour to the rewards it receives for correct answers
Prompt: “Use reinforcement learning to generate an answer to the following question [insert question]”
Chapter 20:Curriculum learning promptsCurriculum learning is a technique that allows a model to learn a complex task by first training with simpler tasks and gradually increasing the difficulty level.To use curriculum learning prompts with ChatGPT, you should provide the model with a sequence of tasks that gradually increase in difficulty.The prompt should also include information about the desired outcome, such as the task to be completed and any special requirements or constraints.This technique is useful for tasks such as natural language processing, image recognition and machine learning.Examples of prompts and their formulas:Example 1:Curriculum learning for text generationTask:Generate text that matches a specific styleInstructions:The model should be trained on simpler styles before moving on to more complex stylesPrompt formula: “Use curriculum learning to generate text that matches the following styles [insert styles]in the following order [insert order]”Example 2:Curriculum learning for language translationTask:Translate text from one language to anotherInstructions:The model should be trained on simpler languages before moving on to more complex languagesPrompt: “Use curriculum learning to translate text from the following languages [insert languages]in the following order [insert order]”Example 3:Curriculum learning for question answeringTask:Generate the answer to a question
Instructions:The model should first be trained on simpler questions before moving on to more complex questionsPrompt: “Use curriculum learning to generate answers to the following questions [insert questions]in the following order [insert order]”
Chapter 21:Sentiment analysis PromptsSentiment analysis is a technique that allows a model to determine the emotional tone or attitude of a text, such as whether it is positive, negative, or neutral.To use sentiment analysis prompts with ChatGPT, you should present the model with a text and ask it to classify it based on its sentiment. The prompt should also include information about the desired output, such as the type of sentiment to be recognised (e.g. positive, negative, neutral) and any specific requirements or constraints.Examples of prompts and their formulas:Example 1:Sentiment analysis of customer reviewsTask:Determine the sentiment of customer reviewsInstructions:The model should classify the reviews as positive, negative or neutralPrompt formula: “Perform a sentiment analysis on the following customer reviews [insert reviews]and classify them as positive, negative or neutral.”Example 2:Sentiment analysis of tweetsTask:Determine the sentiment of tweetsInstructions:The model should classify the tweets as positive, negative or neutralPrompt: “Perform a sentiment analysis on the following tweets [insert tweets]and classify them as positive, negative or neutral. “Example 3:Sentiment analysis of product reviewsTask:Determine the sentiment of product reviewsInstructions:The model should categorise the reviews as positive, negative or neutral
Prompt: “Perform a sentiment analysis of the following product reviews [insert reviews] and categorise them as positive, negative or neutral. “This technique is useful for tasks such as natural language processing, customer service and market research.
Chapter 22:Named entity recognition promptsNamed entity recognition (NER) is a technique that allows a model to identify and classify named entities in a text, such as people, organisations,places and dates.To use named entity recognition prompts with ChatGPT, you should present the model with a text and ask it to identify and classify named entities in that text.The prompt should also include information about the desired output, such as the types of entities to be identified (e.g. people, organisations, places, data) and any specific requirements or constraints.Prompt examples and their formula:Example 1:Recognise named entities in a news articleTask:Identify and classify named entities in a news articleInstructions:The model should identify and classify people, organisations,places and dataPrompt formula: “Perform named entity recognition for the following news article [insert article] and identify and classify people, organisations,places and data.”Example 2:Recognise named entities in a legal documentTask:Identify and classify named entities in a legal documentInstructions:The model should identify and classify people, organisations,places and dataPrompt: “Perform named entity recognition for the following legal document [insert document] and identify and classify people, organisations,places and data. “Example 3:Recognise named entities in a scientific paperTask:Identify and classify named entities in a scientific paper
Instructions:The model should identify and classify people, organisations,places and dataPrompt: “Perform named entity recognition in the following research paper [insert document] and identify and classify people, organisations,places and data.”
Chapter 23:Text Classification PromptsText classification is a technique that allows a model to categorise text into different classes or categories.This technique is useful for tasks such as natural language processing, text analysis and sentiment analysis.It is important to note that text classification is different from sentiment analysis.Sentiment analysis focuses on determining the mood or emotion expressed in a text.Sentiment analysis is often used in the context of customer reviews,posts on social media and other forms of text where the mood expressed is important. To use text classification prompts with ChatGPT, the model should be given a text and asked to classify it based on given categories or labels.The prompt should also include information about the desired output, such as the number of classes or categories, and any specific requirements or constraints.Examples of prompts and their formula:Example 1:Text classification of customer reviewsTask:Classify customer reviews into different categories such as electronics, clothing and furnitureInstructions:The model should classify the reviews based on their contentPrompt formula: “Perform text classification of the following customer reviews [insert reviews]and classify them into different categories such as electronics, clothing and furniture based on their content.”Example 2: Text classification of news articlesTask: Classify news articles into different categories such as sports, politics and entertainment
Instructions:The model should classify the articles based on their contentPrompt: “Perform a text classification of the following news articles [insert article] and categorise them into different categories such as sports, politics and entertainment based on their content. “Example 3: Text classification of emailsTask: Classify emails into different categories such as spam, important or urgentInstructions: The model should classify the emails based on their content and senderPrompt: “Perform text classification on the following emails [insert the emails] and classify them based on their content and sender into different categories such as spam, important or urgent”
Chapter 24:Text generation promptsText generation prompts are related to several other prompt techniques mentioned in this book, such as:Zero, One and Few ShotPrompting,Controlled generation Prompts,Translation Prompts,Language Modelling Prompts,Sentence Completion Prompts.All these prompts are related to each other as they all involve the generation of text. However, they differ in the way the text is generated and in the specific requirements or constraints placed on the generated text.Text generation prompts can be used to optimise an already trained model or to train a new model for specific tasks.Examples of prompts and their formulas:Example 1:Text generation for story writingTask:Generate a story based on a given promptInstructions:The story should be at least 1000 words and contain a certain number of characters and a plotPrompt formula: “Generate a story of at least 1000 words, including characters [insert characters]and a plot [insert plot]based on the following prompt [insert prompt].”Example 2:Text generation for language translationTask:Translate a given text into another languageInstructions:The translation should be accurate and idiomaticPrompt: “Translate the following text [insert text]into [insert target language]and make sure it is accurate and idiomatic. “Example 3:Text generation for text completionTask:Complete a given textInstructions:The generated text should be coherent and consistent with the input text
Prompt: “Complete the following text [insert text]and make sure it is coherent and consistent with the input text. “Chapter 26: Word prediction prompts
Conclusion As we have shown in this book, prompt engineering is a powerful tool for obtaining high quality responses from language models such as ChatGPT. By carefully crafting prompts that incorporate various techniques, we can get the model to generate text that is tailored to our specific needs and requirements.In Chapter 2, we looked at how to use instructional prompts to give the model clear and concise instructions.In Chapter 3, we looked at how role prompts can be used to generate text in a particular voice or style.In Chapter 4, we looked at how standard prompts can be used as a starting point for optimising the model’s performance.We also looked at various advanced prompting techniques, such as Zero, One and Few Shot Prompting, Self-Consistency, Seed-Word Prompt, Knowledge Generation Prompt, Knowledge Integration Prompts, Multiple Choice Prompts, Interpretable Soft Prompts, Controlled generation Prompts, Question-answering Prompts, Summarization Prompts, Dialogue Prompts, Adversarial Prompts, Clustering Prompts, Reinforcement Learning Prompts,Curriculumlearning Prompts,Sentiment analysis Prompts,Named Entity Recognition Prompts and Text Classification PromptsEach of these techniques can be used in different ways to achieve a wide range of different results. As you continue to work withChatGPT and other language models, it’s worth experimenting with different combinations of techniques to see what works best for your particular use case.Finally, feel free to check out other books I have written on other topics. Thank you for reading the entire book.
About the AuthorIbrahim John Ibrahim John is the author of “The Art of Asking ChatGPT for High-Quality Answers:A Complete Guide to Prompt EngineeringTechniques”.He was born in Tanzania and is a well-known personality in the field of technology and business.He is the founder of three successful companies:NzundaTechnologies Limited,Kingbest Companye Limited and AgrasaAgriculture Limited.With his extensive knowledge and experience in the field of language modelling, Ibrahim brings a unique perspective to the subject of prompt engineering and its applications in language modelling. He is passionate about sharing his knowledge and experience with others and is committed to helping people understand and utilise the power of ChatGPT and other modern language models.
