LLM Communication Content Generation Using Organization Intelligence
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Solution Overview
Problem
The process of personalizing cold emails for multiple target organizations is labor-intensive, time-consuming, and prone to inconsistencies due to the variability in individuals' skills in synthesizing information, leading to inefficiencies and increased costs.
Innovation Solution
A communication content generation system using large language models (LLMs) that leverages an organization database to automate the personalization process, allowing users to select and modify data points, and generate tailored communication content efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If employees manually research and personalize cold emails for multiple target organizations, then the content can be tailored to recipient needs, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service by allowing employees to select target organizations from a database and automatically receiving personalized email drafts without manual research. The system performs the time-consuming synthesis of organization information, recent news, and relevant data points automatically, freeing employees from manual preparation work while maintaining personalization quality.
Solution Approach 2:
The patent replaces the mechanical manual process of researching organization websites, news articles, and CRM systems with an automated system that uses large language models. The LLM synthesizes information from multiple sources and generates personalized email content automatically, substituting human manual labor with automated AI-based processing.
2Manufacturing precision
If employees manually synthesize information from multiple sources, then personalized content can be created, but quality varies due to individual skill differences
Solution Approach 1:
The system changes the parameter of information synthesis from manual human processing to automated AI-based processing. The large language model consistently applies the same synthesis methodology to all target organizations, ensuring uniform quality and consistency in email content generation regardless of the employee's individual skills or experience level.
Solution Approach 2:
The patent replaces the variable-quality manual synthesis process with a standardized automated system. The LLM follows consistent guidelines and processing logic for synthesizing organization information, recent news, and relevant data points, eliminating the variability in quality that arises from different employees' skills and experiences.
3Productivity
If employees send multiple prospecting emails daily, then outreach volume increases, but the repetitive manual process leads to inefficiencies and increased costs
Solution Approach 1:
The system enables employees to send high volumes of personalized emails efficiently by automating the preparation process. Employees simply select target organizations from the database and receive ready-to-send personalized drafts instantly, eliminating the time-consuming manual research and synthesis required for each email while maintaining personalization quality.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing organization information, recent news, and relevant data points in the database before emails need to be sent. When an employee selects a target organization, the LLM has already synthesized the necessary information and generated the personalized content, eliminating the need for manual preparation at the moment of sending.
Data Source
AI summary
Various embodiments described herein provide for systems, methods, devices, instructions, and like for generating communication content using one or more large language models (LLMs). In particular, some embodiments provide a communication content generation system that generates content for a communication to a target organization using one or more LLMs and information regarding the target organization provided by an organization database, which can comprise curated organization-intelligence data.