AI Workflow System for Reducing Hallucinations via Stepwise Validation
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Solution Overview
Problem
Current generative AI and large language models suffer from hallucinations, where outputs are fabricated and appear accurate, due to a lack of intrinsic understanding of 'correct' or 'incorrect' predictions.
Innovation Solution
A model-agnostic, interaction-design-oriented approach is implemented, which involves generating step-wise interactions with language models and tailoring user interfaces to display data management and user queries as workflow steps, optimizing outputs through feedback and prompt engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AI models generate outputs directly without step-by-step workflows, then generation speed is maintained, but hallucination accuracy increases
Solution Approach 1:
The patent segments the AI output generation process into multiple discrete steps (Step 1, Step 2, Step 3, etc.), where each step produces a partial output that is validated before proceeding to the next step. This segmentation allows the system to maintain higher accuracy by checking intermediate results while managing the time cost through efficient step execution.
Solution Approach 2:
The system performs preliminary actions by generating and validating intermediate outputs at each step before finalizing the complete output. This preliminary validation approach prevents hallucinations from propagating through the entire generation process, improving reliability while the structured workflow manages time consumption.
2Reliability
If step-by-step workflows are implemented to reduce hallucinations, then output accuracy improves, but system complexity increases
Solution Approach 1:
By segmenting the generation process into standardized steps with clear input-output relationships, the patent manages complexity through modularity. Each step is a discrete unit that can be independently validated and controlled, making the overall complex workflow more manageable and less error-prone.
Solution Approach 2:
The system implements feedback mechanisms at each step where intermediate outputs are validated against expected criteria before proceeding. This feedback loop reduces the need for complex centralized control by enabling local validation decisions, thereby managing system complexity while maintaining high output accuracy.
3Reliability
If prompt engineering is used to improve model outputs, then accuracy improves, but development time increases
Solution Approach 1:
The patent applies prompt engineering techniques in advance to create optimized prompts for each step of the workflow. By performing this optimization work preliminarily during system setup rather than during runtime, the system achieves high prediction accuracy while minimizing the time cost during actual operation.
Solution Approach 2:
The system optimizes prompt parameters and configurations during the workflow design phase to improve prediction accuracy. By adjusting these parameters beforehand and establishing a structured step-by-step process, the patent reduces the need for extensive iterative prompt engineering during deployment, thereby balancing accuracy with development time.
Data Source
AI summary
Provided are systems and methods for AI management configured to guide interactions between end users systems and generative AI services. Various examples can manage data operations, among other options. In various embodiments, an AI management system is configured to host an interactive session via generating workflow steps for controlling interaction with AI models. The interactive session is configured to identify data management operations on user specified data sources. The AI management system can be used to analyze the data sources specified to generate a canonical data format spanning the multiple data sources, generate code for mapping the data sources into a canonical format, normalize the resulting data, cleanse the resulting data, validate the resulting data, and automatically generate code for each such function that can then be triggered by users interacting with the user interface.