Collaborative Prompt Building via AI Facilitator
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Solution Overview
Problem
Creating effective and high-quality prompts for generative artificial intelligence (GAI) models is challenging due to the need for linguistic expertise, context comprehension, and understanding of desired outcomes, as well as considerations for clarity, specificity, contextual relevance, language understanding, task complexity, domain knowledge, and user variability.
Innovation Solution
A collaborative prompt building architecture that leverages the principle of diversity of thought and roles, using a dynamic and self-guided process facilitated by an AI-based facilitator. This approach combines decision trees, knowledge graphs, and neural networks to identify key decisions, ensure accurate reflection of concepts and relationships, and capture decisions to generate high-quality results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single user creates prompts independently, then the process is simple and quick, but the quality and effectiveness of prompts deteriorates due to lack of diverse perspectives and expertise
Solution Approach 1:
The patent merges multiple user perspectives and expertise into a single collaborative prompt building process. The system combines inputs from users with different roles (e.g., domain experts, linguists, developers) to create prompts that benefit from diverse thinking patterns and knowledge bases, thereby improving prompt quality while maintaining efficiency through automated coordination.
Solution Approach 2:
The patent introduces an AI-based facilitator as an intermediary that coordinates the collaborative prompt building process. This facilitator manages the workflow, synthesizes inputs from multiple users, and ensures that diverse perspectives are effectively integrated into the final prompt, resolving the contradiction between simple individual creation and high-quality collaborative output.
2Manufacturing precision
If multiple users collaborate on prompt building, then prompt quality improves through diverse expertise, but the process complexity and coordination difficulty increases
Solution Approach 1:
The AI-based facilitator serves as a mediator that simplifies the coordination complexity of multi-user collaboration. It automatically manages the workflow, assigns tasks based on user expertise, synthesizes inputs, and coordinates contributions from multiple users, thereby reducing the operational complexity while maintaining high prompt quality through diverse perspectives.
Solution Approach 2:
The system enables self-service collaboration where users can contribute their expertise independently without requiring extensive manual coordination. The automated facilitator handles the synthesis and integration of inputs, allowing users to focus on providing domain-specific knowledge while the system manages the complexity of combining multiple perspectives into a cohesive prompt.
3Measurement precision
If prompts are refined through multiple decision points, then the accuracy and relevance of prompts improves, but the time required for prompt creation increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying relevant decision points and preparation areas before the main prompt creation process. The AI facilitator anticipates potential refinement needs and prepares structured questions and evaluation criteria in advance, allowing the collaborative team to focus on substantive decisions rather than procedural coordination, thereby reducing overall time while maintaining high accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the AI facilitator continuously monitors the prompt development process and provides real-time guidance. It identifies when additional refinement is needed and when the prompt is sufficient, allowing the collaborative team to efficiently allocate time to high-impact decision points while avoiding unnecessary refinements, thus balancing accuracy with time efficiency.
Data Source
AI summary
Aspects of the invention include techniques for collaborative prompt building for generative artificial intelligence models. A non-limiting example method includes receiving, from a client, a prompt for a large language model. A decision tree is built to determine one or more decision points for refining the prompt and a knowledge graph is built having one or more nodes associated with a feature of the prompt. The method includes delivering, to the client, a challenge comprising a query associated with at least one of the one or more decision points and the one or more nodes, receiving, from the client, an answer to the challenge, and delivering, to the client, a refined prompt by modifying the prompt using the answer to the challenge.