LLM Content Retrieval Architecture for Low-Latency Query Accuracy
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Solution Overview
Problem
Computing systems face challenges in efficiently and accurately interpreting user queries due to nuanced contexts and complex data sets, leading to delays, latency, and unnecessary resource utilization in delivering digital content.
Innovation Solution
A large language model architecture utilizing natural language processing models, such as conversational or chat-based systems, to manage diverse data formats and structures, adapt to evolving user needs, and deliver personalized content by understanding syntax and sentence structure, incorporating entity recognition and generative models for dynamic and context-sensitive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing models are used to interpret user queries, then accuracy in understanding nuanced contexts is improved, but computational resources and processing time increase
Solution Approach 1:
The system segments the query processing into multiple specialized components: intent classification module, entity recognition module, and natural language interpretation module. Each component handles specific aspects of query understanding independently, improving accuracy while distributing computational load efficiently across modular units rather than requiring one monolithic complex model to handle all processing.
2Measurement precision
If comprehensive data sets are processed to deliver relevant content, then result accuracy is improved, but latency increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing data sets before they are needed for query resolution. Data is organized into structured formats with metadata tags and relationships established in advance, allowing the query processing system to quickly retrieve and evaluate relevant information without performing extensive processing at query time, thus reducing latency while maintaining comprehensive data coverage.
3Adaptability or versatility
If multiple processing models are used to handle diverse data formats, then adaptability is improved, but system complexity increases
Solution Approach 1:
The system implements a universal data processing framework that can handle multiple data formats and structures through a single integrated architecture. The framework includes standardized interfaces and transformation layers that adapt incoming data regardless of format, allowing the same core processing models to work with diverse data types without requiring separate specialized systems for each format, thus maintaining adaptability while controlling complexity.
Data Source
AI summary
Large language model architecture for delivering digital content is provided. A system can receive a query indicating a request for document objects, and criteria for selection of the document objects. The system obtains first data objects each having a first structure in a first format identifying portions of the one or more document objects. The first data objects can be searchable according to the one or more criteria via the first structure. The system provides input to a large language model (LLM) including the query, the first data objects, and second data objects. The second data objects can have a second structure in a second format that is compatible with the LLM. The system generates a reply to the query identifying a set of the one or more document objects that satisfies the one or more criteria for selection.