Knowledge Graph-Enhanced RAG for Selective LLM Context Retrieval
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Solution Overview
Problem
Large language models (LLMs) struggle with nuanced concept analysis in transcripts, leading to inefficient use of computing resources for decision-making tasks.
Innovation Solution
Implementing a retrieval augmented generation (RAG) model that uses a knowledge graph-enhanced retrieval system to manage transcripts, embedding data and applying prompts to LLMs for accurate responses, optimizing computing resource use by determining when enhanced context generation is necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used for evaluating transcripts, then decision-making capability is improved, but computing resource efficiency deteriorates due to unnecessary processing of nuanced concepts
Solution Approach 1:
The system segments the transcript evaluation task into two distinct components: (1) factual information extraction handled by the RAG system, and (2) nuanced concept analysis handled by the LLM. This segmentation allows each component to focus on its strengths while avoiding unnecessary computational overhead.
Solution Approach 2:
The RAG system acts as an intermediary between the transcript data and the LLM. It pre-processes the transcript to extract factual information and contextual relationships, then presents this structured information to the LLM for decision-making analysis, thereby reducing the LLM's computational burden.
2Measurement precision
If enhanced context generation is always applied, then response accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system applies enhanced context generation selectively rather than universally. The RAG system determines whether enhanced context is necessary based on the specific query and transcript content, applying it only when needed to achieve accurate responses while avoiding unnecessary computational expenditure.
Solution Approach 2:
The system dynamically adjusts the level of context enhancement based on query characteristics. By changing the parameter of context generation intensity according to the specific evaluation task, the system optimizes the balance between response accuracy and computing resource consumption.
Data Source
AI summary
A method for managing user queries applied to a large language model (LLM) includes obtaining a user query and in response to obtaining the user query: identifying a state of the user query, wherein the state is based on whether the user query is associated with a previous user query, making a determination, based on the state, that the user query indicates an enhanced context generation, in response to the determination, performing a semantic search on a vectorized database to obtain a set of relevant documents, performing an enhanced search on the set of relevant documents using a knowledge graph to obtain enhanced context, embedding the enhanced context to the user query to obtain a finalized prompt, and applying the finalized prompt to a large language model (LLM) of the data system to obtain a finalized result, and providing the finalized result to the client device.