AI System Orchestration for LLM Consistency
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Solution Overview
Problem
Large language models (LLMs) are opaque, imprecise, and inconsistent, making them challenging to debug and integrate consistently into systems, especially due to complex interactions involving multiple back-and-forth responses that complicate consistency and predictability.
Innovation Solution
An Artificial Intelligence System (AIS) is configured to activate LLMs and other AI processes via natural language prompts, using an orchestration system that selects and executes appropriate data services, including plugins, to improve user interactions by managing user inputs, processing data, and presenting updates through interactive interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are used for conversational interactions, then user engagement and natural language processing capability are improved, but consistency and predictability of responses deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the user and the LLM that includes prompt management, response validation, and contextual control mechanisms. This intermediary structure allows the system to maintain the natural language processing benefits while adding layers of control to improve response consistency and predictability through structured prompt templates and response filtering.
2Productivity
If complex calls to LLM involve multiple back-and-forth responses, then task completion capability is improved, but debugging difficulty and system complexity increase
Solution Approach 1:
The patent segments the complex multi-turn interaction into discrete, manageable components including individual prompt templates, response validation rules, and contextual state management units. Each segment can be independently tested, validated, and debugged, reducing the overall system complexity while maintaining the capability to complete complex tasks through coordinated execution of these segmented components.
3Reliability
If LLM responses are made more precise and consistent, then reliability is improved, but flexibility and conversational capability may deteriorate
Solution Approach 1:
The patent implements dynamic prompt templates and contextual parameters that allow the system to adjust between precision and flexibility based on the specific interaction context. The system can dynamically select between structured templates for consistency and more open-ended prompts for flexibility, with the ability to transition between these modes based on task requirements and conversational state.
Data Source
AI summary
A data orchestration system can be used to respond to natural language prompts (e.g., user submitted prompts) where the response involves a data processing workflow being executed using one or more data processing services (e.g., microservices of a data processing platform or software). This can provide for execution of data processing workflows (e.g., complex workflows) without a user needing to specify the particular data processing services that are included in the data processing workflows. This can cause new functionality to be available to a user (e.g., to a user who lacks the technical skillset to specify the relevant data processing services without use of the systems and methods disclosed herein), and/or can dramatically reduce the time required to orchestrate the data processing services that are included in the data processing workflows.