Generative AI Message Content Suggestions
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Solution Overview
Problem
Conventional message creation systems face challenges in efficiently generating messages with high acceptance probabilities, as they require manual customization and are not scalable for high-volume senders.
Innovation Solution
The use of generative artificial intelligence, specifically generative language models, to automate the process of message suggestion generation, leveraging dynamic data retrieval and prompt engineering techniques to create personalized and diverse message content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual customization is used for message creation, then message acceptance probability is improved, but productivity deteriorates
Solution Approach 1:
The system creates templates from high-performing historical messages and replicates their successful patterns. The template extraction process identifies and copies effective message structures, wording, and formatting from messages that achieved high acceptance rates, then applies these copied patterns to generate new messages at scale.
Solution Approach 2:
The system dynamically adjusts message parameters such as tone, length, formatting, and content emphasis based on recipient profiles and historical performance data. By changing these parameters systematically rather than manually customizing each message, the system maintains high acceptance probabilities while improving generation efficiency.
2Adaptability or versatility
If manual customization is used for message creation, then message diversity is improved, but device complexity deteriorates
Solution Approach 1:
The message creation process is segmented into distinct components: template selection, parameter adjustment, content generation, and quality filtering. This segmentation allows the system to manage complexity by handling each aspect separately through automated rules and algorithms, rather than requiring complex manual customization for each message.
Solution Approach 2:
The system automatically generates diverse message content by self-adjusting parameters and selecting templates based on recipient characteristics and historical data. The automated diversity generation eliminates the need for complex manual intervention while maintaining message variety through algorithmic parameter variation and template selection.
3Productivity
If automated generation is used for messages, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system incorporates feedback loops where historical message performance data is continuously analyzed to refine template effectiveness and parameter settings. Messages generated by the system are evaluated against acceptance metrics, and this feedback is used to automatically adjust future message generation parameters, ensuring consistent quality improvement while maintaining high productivity.
Solution Approach 2:
The system performs preliminary actions by pre-processing and analyzing historical message data to extract successful patterns before actual message generation. Templates are pre-configured with optimal parameters based on historical analysis, and recipient profiles are pre-segmented into categories, allowing consistent quality application during high-volume message generation without real-time manual intervention.
Data Source
AI summary
Embodiments of the disclosed technologies are capable of generating diverse suggested message content. The embodiments describe generating a message plan comprising attribute data and section data. The embodiments further describe inputting the message plan as a prompt to a first generative model. The first generative model is fine-tuned using a training message plan. The training message plan comprises an ordered sequence of training attribute data and training section data. The training attribute data and training section data are extracted from historic messages or generated messages. The embodiments further describe generating, by the first generative model, message content suggestions based on the attribute data and section data.