LLM Context Retrieval for Human-AI Interaction
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Solution Overview
Problem
Large Language Models (LLM) face limitations such as statelessness, long context limitations, timeliness of model data, and inability to provide accurate domain-specific information, leading to inefficient human-AI interactions and increased user input requirements.
Innovation Solution
A human-AI interaction method and system that pre-manages domain-specific information, preprocesses user requests, and retrieves relevant context information to supplement LLM processing, enabling automatic contextualization and improving interaction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users supplement background information and use text templates in first inquiry, then LLM can provide better feedback, but user input complexity and time consumption increase
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and preparing domain-specific information and contextual data before the user submits their inquiry. The retrieval module searches knowledge bases and the information processing module prepares relevant context, so when the user makes their first inquiry, the necessary background information is already in place, eliminating the need for users to manually supplement backgrounds or use text templates.
Solution Approach 2:
The system serves itself by automatically managing the context information lifecycle. The retrieval module autonomously searches for relevant domain-specific information, the information processing module automatically processes and structures this information, and the state management module handles context storage and retrieval. This self-service mechanism eliminates the need for user intervention in context management tasks.
2Measurement precision
If LLM processes more contextual information, then domain-specific accuracy improves, but token limit is exceeded
Solution Approach 1:
The system extracts only the most relevant domain-specific information from the knowledge base using the retrieval module. Instead of processing all available contextual information, the system selectively retrieves and processes only those pieces of information that are directly relevant to the user's inquiry, thereby maintaining domain-specific accuracy while keeping token consumption within limits.
Solution Approach 2:
The system applies local quality by providing different levels of information detail in different contexts. The information processing module structures retrieved information to provide comprehensive domain-specific details where needed while condensing or omitting less critical information, ensuring high accuracy in domain-specific responses without uniformly increasing token quantity across all contexts.
3Speed
If LLM is stateless and processes each query independently, then processing speed is fast, but multi-round conversation context management becomes complex
Solution Approach 1:
The state management module acts as an intermediary between the stateless LLM and the multi-round conversation requirements. It maintains context information in external storage, retrieves relevant context before each query, and injects it into the prompt, thereby enabling context-aware multi-round conversations while keeping the LLM itself stateless and fast-processing.
Solution Approach 2:
The system segments the context management function from the LLM processing function. The state management module handles context storage, retrieval, and injection as a separate component, while the LLM focuses on fast query processing. This segmentation allows each component to optimize for its specific function without the other's constraints.
Data Source
AI summary
A human-artificial intelligence (AI) interaction method based on large language model (LLM) is provided. Pre-management is performed on domain-specific information. When receiving a user problem or a user request, the user question or the user request is preprocessed. The domain-specific information is added to a retrieval scope for retrieval to find an information fragment that is most similar to the user problem or the user request. The information fragment is processed as required to obtained a processed information fragment, and the processed information fragment is taken as the context information of the user problem or the user request. The context information and problem information are transmitted to the LLM for processing according to a LLM interface usage method or requirements. A human-AI interaction system includes a pre-managing module, a problem preprocessing module, a retrieval module, an information fragment processing module, and a transmitting module.