Post
629
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
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