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Data Generation Prompt

GPTClaudeDeepSeek··642 copies·updated 2026-07-14
data-generation-prompt.prompt
Your task is to formulate exactly {num_test_output} questions from given context and query provide the answer to each one.

End each question with a '?' character and then in a newline write the answer to that question using only 
the context and query provided. 
The generated question should be extracted from the context related to the query but if you didn't get enough context extract on topics that are related to the query.
The output questions should not be less that {num_test_output}

The output MUST BE in a json format. 

example:
[
{
    "user": "What is the name of the company?",
    "assistant": "Google"
},
{
    "user": "What is the name of the CEO?",
    "assistant": "Sundar Pichai"
}
]

Each question must start with "user:".
Each answer must start with "assistant:".


The question must satisfy the rules given below:
1.The question should make sense to humans even when read without the given context.
2.The question should be fully answered from the given context.
3.The question should be framed from a part of context that contains important information. It can also be from tables,code,etc.
4.The answer to the question should not contain any links.
5.The question should be of moderate difficulty.
6.The question must be reasonable and must be understood and responded by humans.
7.Do no use phrases like 'provided context',etc in the question
8.Avoid framing question using word "and" that can be decomposed into more than one question.
9.The question should not contain more than 10 words, make of use of abbreviation wherever possible.
    
context: {context}
query: {query}

fill the variables

This prompt has 3 variables. Pro fills them into a ready-to-paste prompt for you — no manual find-and-replace.

{num_test_output}{context}{query}
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when to use it

Community prompt sourced from the open-source GitHub repo GetachewAbebe/Prompt_Engineering_using_Precision_RAG (MIT). A "Data Generation Prompt" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.

tags

productivitycommunitydeveloper

source

GetachewAbebe/Prompt_Engineering_using_Precision_RAG · MIT