LLM Copilot Context Provider for Business Intelligence
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Solution Overview
Problem
Existing solutions for providing business context to large language model (LLM) chatbots are inadequate, leading to inefficient extraction of actionable business intelligence and poor decision-making in enterprises.
Innovation Solution
An LLM copilot system that acts as an intelligent front-end to an LLM chatbot, injecting data from applications, benchmarks, machine learning insights, and business rules into LLM prompts to enhance the accuracy and relevance of responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM chatbots are used for business intelligence extraction, then accessibility and conversational capability are improved, but context relevance and accuracy deteriorate
Solution Approach 1:
The patent introduces a context provider as an intermediary component that sits between the user's questions and the LLM. This mediator automatically retrieves relevant business context from databases, documents, and other sources, then injects it into the LLM prompt. This resolves the contradiction by maintaining the conversational interface while ensuring high context relevance through automated information gathering and injection.
Solution Approach 2:
The system performs preliminary actions by proactively retrieving and preparing relevant business context before the LLM generates its response. The context provider queries databases and fetches documents in advance based on the user's question, so that when the LLM processes the prompt, relevant information is already ready. This preliminary context gathering ensures accuracy without delaying the conversational flow.
2Measurement precision
If detailed business context is provided to LLM, then answer accuracy is improved, but complexity of information processing increases
Solution Approach 1:
The context provider operates autonomously to manage information processing complexity. It self-manages the retrieval, filtering, and injection of business context without requiring manual intervention or complex user input. The system automatically determines what context is needed and gathers it independently, reducing the burden on users while maintaining high accuracy in the LLM's responses.
3Adaptability or versatility
If manual rule construction is required for business context, then customization is improved, but time consumption increases
Solution Approach 1:
The context provider performs self-service by automatically retrieving and injecting business context without requiring users to manually construct rules or provide detailed instructions. The system autonomously queries databases, selects relevant information, and formats it for the LLM prompt, significantly reducing time consumption while maintaining the ability to customize responses through natural language questions.
4Device complexity
If LLM is used without contextual injection, then system simplicity is maintained, but business intelligence extraction effectiveness deteriorates
Solution Approach 1:
The context provider serves as a simple intermediary layer that enhances the LLM's effectiveness without fundamentally altering the system architecture. It acts as a bridge between the user's questions and the LLM's responses, automatically injecting relevant business context while maintaining the overall simplicity of the system. This mediator enables effective business intelligence extraction without requiring complex system redesign.
Data Source
AI summary
A system interposed between a user and a chatbot includes a processor with instructions to: with a conversational interface, receive a user input; with a broker agent, based on the user input and a conversation history, pass the user input to at least one assistant agent (an action agent or a data agent). If the at assistant agent is an action agent: the system determines an action consistent with the user input; and executes the action. If the assistant agent is a data agent: the system fetches data from at least one database; and, based on the user input and the fetched data, generates a reply. The broker agent, in real time, based on the user input and the reply, formulates a chatbot prompt; passes the chatbot prompt to the chatbot; receives an answer from the chatbot; and with the conversational interface, presents the answer to the user.