AI Chatbot Data-Set Routing for Accurate Context Responses
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Solution Overview
Problem
Existing AI/ML chatbots are limited by their inability to customize responses for specific contexts and often provide inaccurate or inconsistent answers due to probabilistic processing, lacking the ability to effectively combine and utilize multiple data sets.
Innovation Solution
A system that enables the creation of AI/ML chatbots by specifying and inferring relationships among multiple data sets, allowing administrators to customize their interactions without re-training models, and combining AI/ML models with non-probabilistic data processing systems for accurate and reliable responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generalized AI/ML models are used, then the system can process and generate natural language text, but the models cannot be customized for particular uses or contexts
Solution Approach 1:
The patent segments the customization process into separate components: the base AI/ML model remains unchanged, while customization is achieved through separate prompt engineering and parameter configuration layers. This allows multiple customized chatbots to be created without retraining the underlying model, resolving the contradiction between adaptability and training complexity.
Solution Approach 2:
The patent introduces an intermediary layer between the AI/ML model and user interactions, consisting of customizable prompts, system instructions, and parameter settings. This intermediary enables customization without modifying the core model, allowing flexible adaptation while avoiding the complexity of retraining.
2Reliability
If multiple data sets are combined, then the chatbot can provide more accurate and context-specific responses, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that manages multiple data sets through structured prompts and contextual framing. This intermediary organizes information from multiple sources without requiring complex integration logic in the AI/ML model itself, maintaining reliability while controlling system complexity.
Solution Approach 2:
The patent performs preliminary organization and structuring of multiple data sets before they are presented to the AI/ML model. Data are pre-processed, contextualized, and formatted into unified prompt structures, which simplifies the integration process and enables accurate responses without increasing model complexity.
3Adaptability or versatility
If AI/ML models are retrained for customization, then the chatbot can be adapted to specific contexts, but computational resources and training time increase
Solution Approach 1:
The patent segments customization from model training, allowing context-specific adaptation through separate prompt and parameter configuration. This enables rapid customization without the time-consuming process of retraining, as the base model remains fixed while surface-level parameters are adjusted.
Solution Approach 2:
The patent creates customized chatbot instances by copying and configuring the same base AI/ML model with different prompts and parameters. This copying approach allows multiple customized versions to be deployed instantly without retraining, preserving adaptability while eliminating training time losses.
4Adaptability or versatility
If probabilistic processing is used, then the system can handle natural language variability, but responses become inaccurate or inconsistent
Solution Approach 1:
The patent performs preliminary structuring and disambiguation of natural language inputs through carefully designed prompts and contextual framing. By pre-processing and clarifying the input context before probabilistic processing, the system maintains its ability to handle language variability while improving response consistency and accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where system responses are evaluated against expected outcomes, and prompts are iteratively refined based on performance data. This feedback loop allows the system to maintain probabilistic processing capabilities while improving consistency over time through prompt optimization rather than model retraining.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for artificial intelligence chatbots that leverage multiple data sets. In some implementations, a system receives a user prompt provided to an interactive application and accesses configuration data that indicates multiple data sets for answering user prompts. The system selects a data set from the multiple data sets based at least in part on the user prompt. The system sends a first request to one or more artificial intelligence and/or machine learning (AI/ML) models, and the system generates result data from the selected data set based on the data processing instructions generated by the AI/ML model. The system sends a second request to the AI/ML model and provides at least a portion of the result data to the AI/ML model. The system provides text that the one or more AI or machine learning models generated in response to the second request.