AI Document Analysis with Dynamic LLM Selection for Query Accuracy
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Solution Overview
Problem
Conventional search engines and AI systems lack the capability to provide in-depth contextual analysis, leading to suboptimal results when dealing with large datasets containing diverse information, and they struggle with domain-specific queries due to varying performance of different LLMs.
Innovation Solution
An AI-based document analysis system incorporating AI-Powered Semantic Search (AIPS) and Advanced Intelligent Knowledge Engine (AIKE) for pre-processing structured and unstructured data, utilizing data taxonomy, knowledge graphs, and dynamic LLM selection to generate accurate responses tailored to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines and AI systems are used for document analysis, then basic search functionality is provided, but in-depth contextual analysis capability is lacking
Solution Approach 1:
The system segments the document analysis task into multiple specialized components: semantic search for contextual understanding, knowledge graph construction for relationship mapping, and dynamic LLM selection for accurate response generation. Each component handles a specific aspect of analysis, collectively achieving in-depth contextual analysis that conventional systems cannot provide
Solution Approach 2:
The system combines multiple AI technologies (semantic search, knowledge graphs, large language models) into a composite analytical framework. This composite approach integrates the strengths of each technology to overcome the limitations of individual systems, enabling both deep contextual analysis and reliable result generation
2Reliability
If a single LLM is used for document analysis, then system simplicity is maintained, but performance varies for domain-specific queries
Solution Approach 1:
The system dynamically selects the most appropriate LLM based on the specific query characteristics and domain requirements. This dynamic selection mechanism allows the system to adapt to different query types and domains, maintaining high accuracy without requiring a static complex multi-LLM architecture for every possible scenario
Solution Approach 2:
The system changes the parameter of LLM selection based on query analysis. By evaluating query characteristics and matching them with suitable LLM profiles, the system optimizes response accuracy for domain-specific queries while keeping the overall system structure manageable through parameter-based routing rather than structural complexity
3Loss of information
If comprehensive data analysis is performed on large datasets, then information completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary semantic search and knowledge graph construction before full document analysis. This preliminary action identifies relevant contexts and relationships in advance, allowing the main analysis to focus on targeted areas rather than processing entire large datasets from scratch, thus maintaining information completeness while reducing processing time
Solution Approach 2:
The knowledge graph serves as an intermediary structure that pre-organizes relationships and contexts from large datasets. This intermediary representation enables rapid querying and analysis by providing a structured overview, reducing the need to process raw data extensively while maintaining comprehensive information coverage
Data Source
AI summary
An Artificial Intelligence (AI) & Generative AI-driven cross-domain document analysis system enables accurate and consistent narratives across a longitudinal timeline for an entity regarding communications in different operational aspects. The document analysis and insight system includes an Artificial Intelligence (AI) powered Search Interface (AIPS) and an Advanced Intelligent Knowledge Engine (AIKE). The AIPS is configured to pre-process documents from structured and unstructured data sources to generate data taxonomies and custom synonym files. The AIKE generates a preliminary evaluation of the various Large Language Models (LLMs) and uses the data taxonomies and custom synonym files to generate prompts that are configured to address limitations of the various LLMs to obtain accurate replies to user requirements.