LLM Query Response Platform With Historical Data Feedback
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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, often resulting in inconsistent answers.
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, incorporating user feedback for model improvement.
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 actions by training the query response model in advance using historical query information and embeddings. This pre-training enables the model to quickly generate accurate responses to new queries without requiring manual intervention for each query, thus reducing response time while maintaining reliability
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 their workload while ensuring answer accuracy through the validation step
2Adaptability or versatility
If multiple organization members provide query responses, then diverse perspectives are obtained, but consistency of answers deteriorates
Solution Approach 1:
The query response model provides a universal foundation that handles various query types consistently. By training on diverse historical query information, the model learns to adapt to different query types while maintaining a consistent response framework, ensuring that all queries are answered using the same methodology and standards
Solution Approach 2:
The system implements feedback mechanisms where query responses are validated by organization members and the results are used to update and improve the model. This continuous feedback loop ensures that the model learns from actual query-response pairs and maintains consistency while adapting to new query patterns and requirements
3Reliability
If manual validation and clarification processes are implemented, then query response accuracy improves, but processing delays increase
Solution Approach 1:
The system performs preliminary validation by having the query response model generate responses that are pre-screened against historical data and embeddings before reaching organization members for final validation. This preliminary filtering reduces the number of queries requiring extensive manual clarification while maintaining high accuracy standards
Solution Approach 2:
The system applies partial validation by having organization members review only the most critical aspects of generated responses rather than complete manual verification of every query. The model handles routine validation automatically, and human reviewers focus on complex or high-stakes queries, optimizing the balance between accuracy and speed
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.