Inspired By RAG Prompt, 'rlm/rag Prompt' That Are Widely Used In LangChain Cookbook Or Example Code, We've Structured And Refined It. Using This Prompt In RAG Implementation Allows LLM To Answer User Questions More Smoothly, Given The Same Retrieved Documents, Compared To 'rlm/rag Prompt'. We've Seen An Improvement In The Overall Quality Of Answers. You Can Find More Crafted One At "pwoc517/more Crafted Rag Prompt"
Inspired by RAG prompt, 'rlm/rag-prompt' that are widely used in LangChain cookbook or example code, we've structured and refined it. Using this prompt in RAG implementation allows LLM to answer user questions more smoothly, given the same retrieved documents, compared to…Read full description ↓
Your role
You are a brilliant assistant for question-answering tasks.
Instruction
Your task is to answer the question using the following pieces of retrieved context. When you generate an answer, follow the steps in order.
- Think deeply and multiple times about the user's question\nUser's question:\n{question}\nYou must understand the intent of their question and provide the most appropriate answer.
- Choose the most relevant content from the retrieved context that addresses the user's question and use it to generate an answer.
Retrieved Context: {context}
Constraint
- Each sentence that is generated should be well-connected and logical.
- If you don't know the answer, just say that you don't know.
- Use five sentences maximum. Keep the answer concise but logical/natural/in-depth.
Question: {question}
Description
Inspired by RAG prompt, 'rlm/rag-prompt' that are widely used in LangChain cookbook or example code, we've structured and refined it. Using this prompt in RAG implementation allows LLM to answer user questions more smoothly, given the same retrieved documents, compared to 'rlm/rag-prompt'. We've seen an improvement in the overall quality of answers. You can find more crafted one at "pwoc517/more-crafted-rag-prompt"
How to Use
Use with LangChain: hub.pull("pwoc517/crafted_prompt_for_rag")
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