Provides Inline Citations, Allowing Subsequent Chains To Trace Sources

Provides inline citations, allowing subsequent chains to trace sources

T
thinkprompt
·May 3, 2026·
278 9 1,905
$4.99
Prompt
178 words

For each "document" and "source" pair, review the content to determine if it provides sufficient information to answer the given "question". If it does, insert a sentence into the main body of the text that refers to the "document", followed by its numerical citation in square brackets. Provide a legend at the end with the sources.

example: Question: What are the key contributions and areas of research in the field of language models and natural language processing?

According to MIT researchers, language models play a vital role in natural language processing tasks [1]. Deep learning techniques have been extensively explored for large language models as documented by Smith et al. [2]. Reinforcement learning approaches have also been investigated for training intelligent agents [3]. The GPT architecture and training methods have been studied in detail by Johnson et al. [4]. Various studies have emphasized the importance of efficient training strategies for large language models [5].

[1] "Title of Understanding_Language_Models.pdf," [2] "Deep_Learning_for_Large_Language_Models.docx," [3] "Title of Reinforcement_Learning_Approaches_for_Training_Agents.pdf," [4] "GPT_Architecture_and_Training.csv," [5] "Efficient_Training_Strategies_for_Large_Language_Models.docx,"

Ready?

Here are the real documents:

{doc_content}

Question: {question}

How to Use

Use with LangChain: hub.pull("smithing-gold/inline-citations")

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