LLM Prompt System for Dynamic Experience Data Routing
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Solution Overview
Problem
Digital content providers face challenges in efficiently collecting, processing, and responding to digital survey data due to the complexity and computational demands of distinguishing between different types of data and accurately routing feedback to the correct segments of an entity or organization, often resulting in inefficiencies and inaccuracies.
Innovation Solution
Implementing intelligent action loops that incorporate machine learning, where a large language model analyzes experience data instances based on defined prompts, enabling efficient and accurate routing of feedback and generation of tailored responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital content providers manually configure step-by-step responses to various data types, then they can handle specific information with specific responses, but the process becomes tedious and computationally demanding
Solution Approach 1:
The system uses large language models to automatically analyze incoming data, determine its type and routing destination, and configure responses without human intervention. The AI model self-services the data routing task that previously required manual configuration, reducing both computational demand and configuration complexity while maintaining accurate routing.
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an intelligent AI-based system. Instead of humans manually configuring response rules for each data type, the large language model automatically processes data and determines appropriate routing and responses, substituting computational intelligence for manual mechanical configuration.
2Adaptability or versatility
If digital content providers customize responses for each type of survey or digital journey data, then they can tailor appropriate responses to specific data types, but they consume high computational resources
Solution Approach 1:
The system employs a universal large language model that can handle multiple data types and routing decisions through a single integrated AI system. Instead of requiring separate computational systems or extensive pre-configured rules for each data type, one multi-functional AI model performs all analysis and routing tasks, reducing overall computational resource consumption while maintaining adaptability to various data types.
Solution Approach 2:
The system changes the operational parameters of data processing by using AI-based semantic analysis instead of traditional rule-based matching. This parameter change allows the system to adapt to different data types dynamically through understanding rather than through pre-configured parameters, reducing the computational overhead of maintaining and executing extensive rule sets for each data type.
3Measurement precision
If digital content providers process large volumes of survey data to identify type, topic, and sentiment, then they can analyze feedback accurately, but significant resources are consumed
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with AI-based analysis. Instead of using conventional algorithms that require significant computational resources to analyze sentiment, topic, and data type, the large language model performs these analyses more efficiently by leveraging its pre-trained linguistic understanding, thereby improving productivity while maintaining or enhancing measurement precision.
Solution Approach 2:
The system performs preliminary analysis by using the large language model's pre-trained knowledge to quickly assess data characteristics before detailed processing. The AI model's pre-existing understanding of language patterns, sentiment, and topics allows it to perform initial classification and analysis more efficiently, reducing the computational resources needed for subsequent detailed processing steps.
4Reliability
If digital content providers use conventional systems to process journey data, then they can identify actions to take, but they struggle with operational flexibility and timeliness
Solution Approach 1:
The system introduces dynamics by using a large language model that can adapt its analysis and routing decisions in real-time based on the specific characteristics of each incoming data instance. Instead of rigid pre-configured rules, the AI model dynamically adjusts its processing approach to match the content and context of the data, enhancing operational flexibility while maintaining accurate action identification and enabling timely responses.
Data Source
AI summary
This disclosure covers systems and methods that define a prompt for a large language model that references dynamic experience data content and based on a model output, determines at least one action to perform. In certain embodiments, by defining a prompt for a large language model and receiving an experience data instance from a respondent device, the disclosed system sends the experience data instance with the defined prompt to the large language model. Further, the disclosed system receives a model output from the large language model, where the first model output is generated based on the large language model analyzing the experience data instance according to the prompt.