Data-Grounded Email Auto-Response With Vector Retrieval and LLM Prompts
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Solution Overview
Problem
Traditional response generation systems face challenges in accurately interpreting complex text data due to the rule-based nature of natural language processing models, leading to misinterpretations and a lack of flexibility in configuring Large Language Models (LLMs) for email responses, which affects the quality and relevance of generated content.
Innovation Solution
The system employs AI data grounding and LLMs to process complex text data by leveraging vast datasets, incorporating context-specific information, and using vector search and nearest neighbor algorithms for efficient retrieval, while allowing user configuration through customizable templates and drag-and-drop functionality for email composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based natural language processing models are used for response generation, then the system structure is simple and easy to implement, but the accuracy and relevance of interpreting complex text data deteriorates
Solution Approach 1:
The patent transitions from rule-based NLP models to LLMs, fundamentally changing the operational parameters and architecture of the text processing system. This parameter change enables the system to handle complex text data with higher accuracy while maintaining ease of implementation through standardized LLM interfaces and prompt engineering frameworks.
2Measurement precision
If LLMs are used for email response generation, then the accuracy and relevance of generated content improves, but the complexity of configuring and steering the LLM increases
Solution Approach 1:
The patent segments the LLM configuration process into distinct components: prompt templates, data grounding modules, quality threshold settings, and response generation parameters. This segmentation allows each component to be independently optimized and managed, reducing the overall complexity while maintaining high content relevance.
Solution Approach 2:
The patent introduces intermediary elements such as prompt templates and data grounding layers that mediate between the user's needs and the LLM's processing. These intermediaries simplify the configuration process by providing structured interfaces and pre-processing mechanisms that guide the LLM without requiring deep technical knowledge of its internal workings.
3Device complexity
If traditional NLP models are used for processing text data, then the system complexity is low, but the ability to adapt to various text styles and formats deteriorates
Solution Approach 1:
The patent employs LLMs with universal capabilities that can handle multiple text styles, formats, and languages through a single unified system. This multi-functionality is achieved through the LLM's training on diverse data and its ability to adapt to different contexts, eliminating the need for separate specialized models for each text type.
Solution Approach 2:
The patent introduces dynamic prompt templates and configuration options that allow the system to adapt to various text styles and formats in real-time. The LLM can dynamically adjust its processing approach based on the input characteristics, providing versatility without requiring a complex array of static models.
Data Source
AI summary
Disclosed herein are system, method, and computer program product aspects for response drafting, grounding, generation, and/or auto-response. A similarity search is performed within a database storing data chunks representing knowledge information that corresponds to a user to obtain top-k data chunks associated with an email from the user. A prompt is generated based on the email, the top-k data chunks, and one or more instructions directing a large language model (LLM) to generate related content for responding to the email. The LLM is then queried with the prompt. In addition, a response to the email is generated based on incorporating the related content into a response template.