Dynamic Prompt Orchestration for Accurate Low-Loop AI Responses
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Solution Overview
Problem
Traditional dynamic prompt orchestration methods for context-driven adaptive user interaction are manual, time-consuming, and prone to human subjectivity, leading to inaccurate results and excessive resource consumption, including electricity and processing capacity, with continuous resubmission loops exceeding contractual limits.
Innovation Solution
An automated system and method for dynamic prompt orchestration that utilizes a prompt library and a two-step approach to match appropriate prompts with AI models, reducing resubmission loops, minimizing resource reliance, and optimizing response time and accuracy by selecting the best prompt and AI model combination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dynamic prompt orchestration is used, then human control and flexibility are maintained, but time consumption increases and accuracy decreases due to human subjectivity
Solution Approach 1:
The system enables automated self-service through the prompt orchestration module that automatically selects optimal prompts and AI models without human intervention. The automated selection process based on inquiry analysis eliminates human subjectivity and significantly reduces time consumption while maintaining high accuracy through systematic evaluation criteria.
Solution Approach 2:
The patent replaces the manual mechanical process of prompt selection with an automated intelligent system. The prompt orchestration module uses algorithmic analysis of inquiries and automated matching with prompt categories, substituting human manual operations with automated computational processes that are faster and more consistent.
2Measurement precision
If continuous resubmission loops are used to improve accuracy, then response quality may improve, but resource consumption including electricity and processing capacity exceeds contractual limits
Solution Approach 1:
The system performs preliminary action by pre-categorizing prompts and pre-evaluating AI model suitability before actual inquiry processing. The prompt library is organized in advance with categorized prompts, and the orchestration module pre-assesses the best matching combinations, eliminating the need for continuous resubmission loops and reducing resource consumption.
Solution Approach 2:
The system implements feedback mechanisms where the prompt orchestration module continuously monitors and learns from the performance of different prompt-AI model combinations. This feedback enables the system to optimize selections over time, achieving high accuracy without excessive resubmissions by learning from past interactions and adjusting future selections accordingly.
3Measurement precision
If multiple AI models are evaluated to find the best match, then response accuracy improves, but processing complexity and time increase
Solution Approach 1:
The patent applies segmentation by dividing the complex task of AI model selection into manageable segments: inquiry analysis, prompt category selection, individual prompt selection, and AI model matching. This segmented approach reduces system complexity by breaking down the evaluation process into discrete, organized steps that can be handled systematically.
Solution Approach 2:
The prompt orchestration module serves as an intermediary between the inquiry and the AI models. It mediates the complex evaluation process by analyzing inquiries, selecting appropriate prompt categories, choosing specific prompts, and matching with suitable AI models. This intermediary layer simplifies the overall system architecture by centralizing the decision-making logic.
Data Source
AI summary
The present disclosure discloses a method and a system of dynamic prompt orchestration. The method includes maintaining a prompt library of artificial intelligence (AI) prompt categories. An inquiry is received and one of the prompt categories appropriate for advancing the received inquiry is selected. Further, based on the selected one of the prompt categories, an AI model from the model registry is selected. Thereafter, based on the selected AI model, a particular individual prompt from the selected one of the prompt categories is selected. The selected particular individual prompt is submitted to the selected AI model. The selected AI model provides information identifying how to respond to the inquiry, wherein the information including at least one action and data supporting the at least one action. Subsequently, the at least one action based on the data is executed and a response is generated.