# Multiple choice quiz generator | [Start Chat](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%22PVAQ7e8kglwUrLe0u07lm%22%2C%22flow%22%3Atrue%7D%7D) This prompt is used to generate {n} multiple choice questions based on long text information {text}. The text can be a summary of a transcript, or excerpt form a book. The output is a JSON to allow integration with code or apps. # Prompt ``` Given the corpus of text, \ generate {{n}} multiple choice questions\ based on the contents of the text. The goal of the these questions is to \ quiz the audience after who have read the text. Make sure to randomize \ the order of the answers for each question and evenly distribute the correct \ answer across the options. Each question should be different and not repeated. \ Format the questions in JSON as follows, make sure to use double quotes:\n \ {{\ "questions": [\ {{\ "question": "Who did X?",\ "options": [\ "A) Answer 1",\ "B) Answer 2",\ "C) Answer 3",\ "D) Answer 4" ],\ "correct_answer": "C) Answer 3", \ "explanation": "Explanation of the correct answer" \ }},\ // More questions...\ ]\ }} The text delimited in triple backticks: ```{{text}}``` ``` ## Welcome Message { "questions": [ { "question": "What is the main purpose of the Think GPT library?", "options": [ "A) Enhancing large language models", "B) Storing external knowledge", "C) Performing code generation", "D) Enabling natural language conditions" ], "correct_answer": "A) Enhancing large language models", "explanation": "The Think GPT library aims to overcome the limitations of large language models (LLMs) and enhance their capabilities." }, { "question": "Which feature of the Think GPT library allows users to fit large amounts of knowledge into the limited context size of the LLM?", "options": [ "A) Long memory", "B) Self-refinement", "C) Compression", "D) Natural language conditions" ], "correct_answer": "C) Compression", "explanation": "The Think GPT library offers two methods for compression, samurai and summarize, to fit large amounts of knowledge into the limited context size of the LLM." }, { "question": "What is the purpose of the LLM library?", "options": [ "A) Summarization", "B) Self-refinement", "C) Code generation", "D) All of the above" ], "correct_answer": "D) All of the above", "explanation": "The LLM library provides functionalities such as summarization, self-refinement, and generative agents." }, { "question": "What parameter can users specify when using LLM for summarization?", "options": [ "A) Max tokens", "B) Target limit", "C) Chunk size", "D) Memory store" ], "correct_answer": "A) Max tokens", "explanation": "Users can specify parameters such as max tokens and target limit for summarization using LLM." }, { "question": "What infrastructure is needed to get started with the library for implementing prompts?", "options": [ "A) Vector database", "B) Cluster setup", "C) OpenAI API key", "D) Memory store" ], "correct_answer": "C) OpenAI API key", "explanation": "Users only need an OpenAI API key and the library installed to get started with the library for implementing prompts." } ] } ## Conversation **ASSISTANT**: { "questions": [ { "question": "What is the main purpose of the Think GPT library?", "options": [ "A) Enhancing large language models", "B) Storing external knowledge", "C) Performing code generation", "D) Enabling natural language conditions" ], "correct_answer": "A) Enhancing large language models", "explanation": "The Think GPT library aims to overcome the limitations of large language models (LLMs) and enhance their capabilities." }, { "question": "Which feature of the Think GPT library allows users to fit large amounts of knowledge into the limited context size of the LLM?", "options": [ "A) Long memory", "B) Self-refinement", "C) Compression", "D) Natural language conditions" ], "correct_answer": "C) Compression", "explanation": "The Think GPT library offers two methods for compression, samurai and summarize, to fit large amounts of knowledge into the limited context size of the LLM." }, { "question": "What is the purpose of the LLM library?", "options": [ "A) Summarization", "B) Self-refinement", "C) Code generation", "D) All of the above" ], "correct_answer": "D) All of the above", "explanation": "The LLM library provides functionalities such as summarization, self-refinement, and generative agents." }, { "question": "What parameter can users specify when using LLM for summarization?", "options": [ "A) Max tokens", "B) Target limit", "C) Chunk size", "D) Memory store" ], "correct_answer": "A) Max tokens", "explanation": "Users can specify parameters such as max tokens and target limit for summarization using LLM." }, { "question": "What infrastructure is needed to get started with the library for implementing prompts?", "options": [ "A) Vector database", "B) Cluster setup", "C) OpenAI API key", "D) Memory store" ], "correct_answer": "C) OpenAI API key", "explanation": "Users only need an OpenAI API key and the library installed to get started with the library for implementing prompts." } ] }