GUIDED DYNAMIC CONSTRUCTION OF LARGE LANGUAGE MODEL (LLM) PROMPTS

Abstract: example solutions for reducing the likelihood of hallucinations by large language models (llms) and improving the relevance of generated text are disclosed. a set of multiple of machine learning (ml) models, each custom-trained to identify a different specific topic within text, dynamically generate multiple topic-specific prompts from a received email in an email thread. a user, responding to the prompts, produces material that is used in conjunction with customer relations management (crm) data and enterprise suite data (e.g., calendar/schedule information) to dynamically generate an llm prompt. an llm, using the prompt, generates output text suitable for a business correspondence, such as a reply email, to the sender of the email. the combination of the responses to the multiple topic-specific prompts and the crm and enterprise suite data in the llm prompt both ensures relevance of the reply correspondence and minimizes the risk of hallucinations.

Inventor(s): Roy EISENSTADT, Abed El Kader ASI, Bar SEGAL, Eyal ZACH, Royi RONEN

CPC Classification: G06F40/40 (ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models ))

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