Multiple choice quiz generator | Start Chat
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.
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}}```
{
"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."
}
]
}
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."
}
]
}