LLM Query Enhancement With Context Validation and Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional query response systems, including those using large language models (LLMs), struggle with flexibility and adaptability, often failing to learn from past interactions and provide accurate responses in dynamic contexts.
Innovation Solution
An automated system integrating a service layer with LLMs and vector databases to enhance query processing, including preprocessing, postprocessing, and feedback learning, ensuring context enrichment and response validation, thereby improving LLM interactions with various products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional query response systems use static databases and algorithms, then they can provide consistent responses, but they lack flexibility and adaptability to new query types and past interactions
Solution Approach 1:
The system implements feedback mechanisms where LLM responses are evaluated against ground truth data, and performance metrics are tracked and used to refine future responses. This feedback loop enables the system to learn from past interactions and improve adaptability while maintaining reliability through continuous validation.
Solution Approach 2:
The patent replaces traditional static database lookup mechanisms with LLM-based generative systems that can dynamically generate responses. This substitution allows the system to handle diverse query types flexibly while maintaining response quality through validation against ground truth data.
2Adaptability or versatility
If LLMs are used to answer queries, then flexibility and adaptability improve, but response accuracy deteriorates for certain query types
Solution Approach 1:
The system validates LLM responses against ground truth data and uses performance metrics to identify and correct inaccuracies. This feedback mechanism enables the system to maintain high response accuracy across different query types while preserving the flexibility and adaptability that LLMs provide.
Solution Approach 2:
The patent introduces an intermediary validation layer between the LLM and the final response output. This intermediary component checks LLM-generated responses against ground truth data and filters out inaccurate responses, thereby maintaining response accuracy while preserving LLM flexibility.
3Speed
If LLMs process queries without context enrichment, then processing speed is faster, but response relevance decreases
Solution Approach 1:
The system performs preliminary context enrichment by retrieving relevant information from databases and other sources before the LLM processes the query. This preliminary action ensures that the LLM has access to necessary context for generating relevant responses while maintaining efficient processing speeds through optimized data retrieval and preparation.
Data Source
AI summary
At least one processor can obtain active time data indicating a duration of work on a project in a software product and preparation time data indicating additional elapsed time between a start and an end of the project. The at least one processor can convert the active time data and the preparation time data into a data entry having a standardized format and store the data entry in a database. The at least one processor can identify context data in the database. The at least one processor can generate a large language model (LLM) prompt comprising a structured combination of a query, the database entry, and the context data, send the LLM prompt to an LLM, and receive a response from the LLM. The at least one processor can validate the response and, in response to the validating, send the response to the software product.