Session Handlers for AI Communications
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Solution Overview
Problem
Existing data management systems face challenges in providing accurate and relevant responses from large language models (LLMs) due to limitations in including all relevant context in prompts and potential scope issues, leading to incomplete or contextually inaccurate responses.
Innovation Solution
The implementation of different classes of handlers for various use cases, which select and instantiate a handler based on the use case or entry point of a communication session with an LLM. These handlers generate use case-specific prompts and include specific function calls and parameters to improve response accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all relevant context is included in LLM prompts, then response accuracy is improved, but prompt length and complexity increase
Solution Approach 1:
The patent segments the prompt into distinct components: a standardized base prompt containing essential LLM instructions, and modular context blocks that are selectively appended based on the specific use case. This segmentation allows the system to maintain a clean, manageable base structure while adding only the necessary context for each scenario, thereby improving response accuracy without proportionally increasing overall prompt complexity.
Solution Approach 2:
The patent performs preliminary action by pre-defining multiple standardized handler classes (e.g., backup handler, restore handler, security handler) that each contain pre-configured context specific to their domain. When a communication session begins, the system instantly selects and applies the appropriate pre-prepared handler, eliminating the need to construct and manage complex prompts from scratch for each use case.
2Adaptability or versatility
If multiple handler classes are implemented for different use cases, then response relevance is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal handler architecture where a single communication manager handles all communication sessions, but selectively instantiates different standardized handler classes based on the session's use case. Each handler class is self-contained and specialized for a specific domain (backup, restore, security, etc.), yet all are managed through a common interface. This allows the system to achieve high adaptability and response relevance while maintaining manageable system complexity through standardized management.
Solution Approach 2:
The system manages complexity by changing parameters dynamically - specifically, which handler class is instantiated and which context blocks are appended to the prompt. The underlying architecture remains constant; only the active handler and included context parameters change based on the communication session requirements. This parameter-based approach allows versatility without proportionally increasing structural complexity.
3Manufacturing precision
If use case specific functions are called, then action accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring all handler classes with their specific functions, parameters, and context during system initialization. When a communication session requires a specific use case, the appropriate handler is already prepared and can be instantly instantiated without dynamic construction. This pre-preparation significantly reduces processing time while maintaining high action accuracy, as the system simply selects from pre-validated options rather than constructing handlers in real-time.
Data Source
AI summary
Methods, systems, and devices for data management are described. A data management system (DMS) may support a communication service that may enable users to ask questions, troubleshoot problems, or initiate workflows. A user may initiate a communication session with the communication service by transmitting a query to the communication service. When a user initiates a chat session with the DMS, for example, via a query, the DMS may instantiate a handler of a handler class based on the use case (e.g., based on contextual information associated with the query). The DMS may use the handler to generate a use case specific prompt for a large language model (LLM) based on the query from the user. The DMS may provide the response from the LLM to the user. Different handler classes may also include use case specific function calls and parameters, which LLMs may use to trigger/initiate/suggest specific actions.