LLM Copilot Pipeline With Plugins for Context-Specific Answers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The application of AI copilots in industries like agriculture is limited due to a lack of specialized training data and the need for context-specific responses that general search engines cannot provide.
Innovation Solution
A comprehensive LLM pipeline is used to generate industry-specific questions and answers by incorporating automatic data ingestion, intelligent data extraction, and a question and answer generation mechanism, allowing users to customize copilots with plugins that provide tailored responses through parallel processing and fine-tuning of models like GPT-4.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general search engines are used to answer queries, then broad information access is improved, but context-specific accuracy and relevance deteriorate
Solution Approach 1:
The patent segments the information retrieval process into multiple specialized components: industry-specific data ingestion, domain-specific knowledge graphs, and targeted question-answering modules. This segmentation allows the system to maintain broad information access while ensuring context-specific accuracy through specialized processing for different domains.
Solution Approach 2:
The patent introduces an intermediary layer between general search and final responses, consisting of domain experts, verification mechanisms, and context-aware filtering systems. This intermediary validates and refines information before presenting it, ensuring both broad coverage and context-specific precision.
2Adaptability or versatility
If AI copilots are trained on general data, then model versatility is improved, but industry-specific response quality deteriorates
Solution Approach 1:
The patent implements a dynamic training framework where the model adapts its knowledge base based on the specific industry context required. The system can dynamically switch between general knowledge modes and industry-specific modes, loading relevant domain data as needed. This allows the model to maintain versatility across industries while achieving high reliability within each specific domain through context-appropriate training data activation.
3Measurement precision
If specialized training data is collected for each industry, then response relevance is improved, but data collection complexity and time deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and structuring industry-specific training data before actual deployment. Knowledge graphs, domain ontologies, and standardized data schemas are prepared in advance for multiple industries. When a specific industry application is needed, the system can rapidly activate pre-prepared data structures rather than collecting data from scratch, significantly reducing deployment time while maintaining high response relevance.
4Measurement precision
If complex data processing pipelines are implemented, then data extraction accuracy is improved, but system complexity and processing time deteriorate
Solution Approach 1:
The patent replaces complex mechanical data processing pipelines with intelligent, automated systems using natural language processing, semantic analysis, and machine learning models. Instead of manual or rule-based extraction mechanisms, the system uses AI-driven approaches that can accurately interpret and extract information from diverse formats without requiring complex preprocessing infrastructure, thereby maintaining high extraction accuracy while reducing system complexity.
Data Source
AI summary
The present disclosure relates to systems and methods for creating a copilot. The copilot uses plugins to provide additional features and functionalities to the copilot. The systems and methods use a large language model (LLM) pipeline to generate a knowledge resource used by the plugins and/or an LLM in the copilot to answer queries from a user.