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276 lines
5.9 KiB
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# 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)
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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.
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# Prompt
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```
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Given the corpus of text, \
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generate {{n}} multiple choice questions\
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based on the contents of the text. The goal of the these questions is to \
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quiz the audience after who have read the text. Make sure to randomize \
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the order of the answers for each question and evenly distribute the correct \
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answer across the options. Each question should be different and not repeated. \
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Format the questions in JSON as follows, make sure to use double quotes:\n \
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{{\
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"questions": [\
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{{\
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"question": "Who did X?",\
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"options": [\
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"A) Answer 1",\
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"B) Answer 2",\
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"C) Answer 3",\
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"D) Answer 4"
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],\
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"correct_answer": "C) Answer 3", \
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"explanation": "Explanation of the correct answer" \
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}},\
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// More questions...\
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]\
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}}
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The text delimited in triple backticks:
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```{{text}}```
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```
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## Welcome Message
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{
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"questions": [
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{
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"question": "What is the main purpose of the Think GPT library?",
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"options": [
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"A) Enhancing large language models",
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"B) Storing external knowledge",
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"C) Performing code generation",
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"D) Enabling natural language conditions"
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],
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"correct_answer": "A) Enhancing large language models",
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"explanation": "The Think GPT library aims to overcome the limitations of large language models (LLMs) and enhance their capabilities."
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},
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{
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"question": "Which feature of the Think GPT library allows users to fit large amounts of knowledge into the limited context size of the LLM?",
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"options": [
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"A) Long memory",
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"B) Self-refinement",
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"C) Compression",
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"D) Natural language conditions"
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],
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"correct_answer": "C) Compression",
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"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."
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},
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{
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"question": "What is the purpose of the LLM library?",
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"options": [
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"A) Summarization",
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"B) Self-refinement",
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"C) Code generation",
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"D) All of the above"
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],
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"correct_answer": "D) All of the above",
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"explanation": "The LLM library provides functionalities such as summarization, self-refinement, and generative agents."
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},
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{
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"question": "What parameter can users specify when using LLM for summarization?",
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"options": [
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"A) Max tokens",
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"B) Target limit",
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"C) Chunk size",
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"D) Memory store"
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],
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"correct_answer": "A) Max tokens",
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"explanation": "Users can specify parameters such as max tokens and target limit for summarization using LLM."
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},
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{
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"question": "What infrastructure is needed to get started with the library for implementing prompts?",
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"options": [
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"A) Vector database",
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"B) Cluster setup",
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"C) OpenAI API key",
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"D) Memory store"
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],
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"correct_answer": "C) OpenAI API key",
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"explanation": "Users only need an OpenAI API key and the library installed to get started with the library for implementing prompts."
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}
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]
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}
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## Conversation
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**ASSISTANT**: {
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"questions": [
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{
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"question": "What is the main purpose of the Think GPT library?",
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"options": [
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"A) Enhancing large language models",
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"B) Storing external knowledge",
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"C) Performing code generation",
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"D) Enabling natural language conditions"
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],
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"correct_answer": "A) Enhancing large language models",
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"explanation": "The Think GPT library aims to overcome the limitations of large language models (LLMs) and enhance their capabilities."
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},
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{
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"question": "Which feature of the Think GPT library allows users to fit large amounts of knowledge into the limited context size of the LLM?",
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"options": [
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"A) Long memory",
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"B) Self-refinement",
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"C) Compression",
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"D) Natural language conditions"
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],
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"correct_answer": "C) Compression",
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"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."
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},
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{
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"question": "What is the purpose of the LLM library?",
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"options": [
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"A) Summarization",
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"B) Self-refinement",
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"C) Code generation",
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"D) All of the above"
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],
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"correct_answer": "D) All of the above",
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"explanation": "The LLM library provides functionalities such as summarization, self-refinement, and generative agents."
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},
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{
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"question": "What parameter can users specify when using LLM for summarization?",
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"options": [
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"A) Max tokens",
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"B) Target limit",
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"C) Chunk size",
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"D) Memory store"
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],
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"correct_answer": "A) Max tokens",
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"explanation": "Users can specify parameters such as max tokens and target limit for summarization using LLM."
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},
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{
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"question": "What infrastructure is needed to get started with the library for implementing prompts?",
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"options": [
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"A) Vector database",
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"B) Cluster setup",
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"C) OpenAI API key",
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"D) Memory store"
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],
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"correct_answer": "C) OpenAI API key",
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"explanation": "Users only need an OpenAI API key and the library installed to get started with the library for implementing prompts."
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}
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]
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}
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