How to Design LLMs that Don’t Need Prompt Engineering	
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How to Design LLMs that Don’t Need Prompt Engineering	
https://mltblog.com/3GAbAQu
https://mltblog.com/3GAbAQu
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Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your query, having to go through a chat session, or prompt engineering. In this article, I explain how to do it, focusing on enterprise corpuses. The strategy relies on four principles:
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Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your query, having to go through a chat session, or prompt engineering. In this article, I explain how to do it, focusing on enterprise corpuses. The strategy relies on four principles:
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➡️ Exact and augmented retrieval
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➡️ Exact and augmented retrieval
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➡️ Showing full context in the response
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➡️ Showing full context in the response
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➡️ Enhanced UI with option menu
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➡️ Enhanced UI with option menu
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➡️ Structured response as opposed to long text
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➡️ Structured response as opposed to long text
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I now explain these principles.
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I now explain these principles.
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Read full article at 
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Read full article at 
https://mltblog.com/3GAbAQu
https://mltblog.com/3GAbAQu
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#xLLM #BondingAI #PromptEngineering
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#xLLM #BondingAI #PromptEngineering
How to Design LLMs that Don’t Need Prompt Engineering	https://mltblog.com/3GAbAQu



Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your query, having to go through a chat session, or prompt engineering. In this article, I explain how to do it, focusing on enterprise corpuses. The strategy relies on four principles:

➡️ Exact and augmented retrieval
➡️ Showing full context in the response
➡️ Enhanced UI with option menu
➡️ Structured response as opposed to long text

I now explain these principles.

Read full article at https://mltblog.com/3GAbAQu

#xLLM #BondingAI #PromptEngineering
👀
John6666