LLM Query Response Platform for Context-Specific Enterprise Answers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for responding to user queries in enterprise organizations are time-consuming, prone to human error, and struggle to keep pace with data volume and regulatory changes, leading to inconsistent answers and high resource requirements.
Innovation Solution
A computing platform using large language models trains a query response model based on historical query information to generate efficient, consistent, and context-specific responses, which can update and improve based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to respond to user queries, then organization members can provide context-specific answers, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary action by training the query response model in advance on historical query information, regulatory documents, and industry data. This pre-training enables the model to quickly generate accurate responses to new queries without requiring time-consuming manual analysis, thus resolving the contradiction between response accuracy and response time.
Solution Approach 2:
The query response model acts as an intermediary between user queries and organization members. The model processes queries and generates draft responses that can be reviewed and validated by organization members, reducing the time and resources required while maintaining accuracy through human-in-the-loop validation.
2Reliability
If manual validation and clarification processes are implemented, then query response accuracy improves, but response delays increase
Solution Approach 1:
The system implements feedback mechanisms where organization members validate and provide feedback on model-generated responses. This feedback is used to continuously refine and update the query response model, improving consistency over time while maintaining high throughput through automated processing of validated response patterns.
Solution Approach 2:
The model performs preliminary response generation before human validation, providing draft responses that are likely to be correct based on historical patterns. This preliminary action reduces the burden on validators and increases overall productivity while maintaining consistency through structured validation processes.
3Adaptability or versatility
If multiple organization members provide query responses, then diverse perspectives are captured, but answer consistency decreases
Solution Approach 1:
The system merges the capabilities of multiple organization members into a single query response model through training on their historical responses and expertise. This consolidation maintains the collective knowledge and adaptability of multiple experts while ensuring uniformity in response delivery through the centralized model.
Solution Approach 2:
The query response model serves as a universal response generator that can handle various types of queries across different domains. It incorporates the expertise of multiple organization members into a single multi-functional system that maintains consistency while adapting to diverse query types through its training data.
4Adaptability or versatility
If conventional systems process user queries, then basic responses can be generated, but they struggle with data volume and regulatory changes
Solution Approach 1:
The system performs preliminary action by continuously training the query response model on updated regulatory documents, industry standards, and historical data. This ongoing pre-training enables the model to adapt to regulatory changes and data volume increases without requiring proportional increases in system complexity or manual resources.
Solution Approach 2:
The system adapts to regulatory changes and data volume increases through parameter changes in the machine learning model, such as updating training data distributions, adjusting model architecture parameters, and refining processing parameters. These parameter adjustments enable adaptability without proportionally increasing system complexity.
Data Source
AI summary
A query response platform may train a large language model to output query responses based on user queries. The platform may receive user queries. The platform may generate query prompts and query responses using the large language model. The platform may receive query feedback corresponding to whether user queries were satisfied. The platform may generate alternative responses based on query feedback. The platform may output a graphical representation corresponding to the query response. The platform may send and/or receive query information corresponding to a user query to third-party devices. The platform may update the large language models based on query information.