Multi-Cloud Generative AI Orchestration for Hallucination Validation
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Solution Overview
Problem
Generative AI models often produce AI hallucinations and inaccurate results, which can be misleading and require techniques to improve their reliability and accuracy.
Innovation Solution
A system that identifies and resolves incongruent results from multiple generative AI models by using machine learning and rules-based decision making, validates data through web searches, and consolidates accurate results using a large language model (LLM) to generate a final response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple generative AI models are used to process user prompts, then the quantity of results increases, but the reliability decreases due to AI hallucinations and incongruent results
Solution Approach 1:
The system segments the result validation process into distinct components: individual model result generation, incongruence detection module, web search validation module, and merging module. Each component handles a specific aspect of the validation pipeline, allowing complex multi-model results to be systematically processed and verified without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary validation layer between multiple AI models and the final output. This intermediary system uses web searches as an independent verification source to check incongruent results, acting as a mediator that resolves conflicts between different model outputs without requiring direct comparison or integration of the models themselves
2Measurement precision
If AI hallucinations are eliminated through validation, then the accuracy improves, but the time required to process results increases
Solution Approach 1:
The system performs preliminary detection of incongruent results using automated rules and comparison algorithms before initiating time-consuming web searches. By pre-identifying which results require validation and which are consistent across models, the system minimizes unnecessary validation steps and reduces overall processing time while maintaining high accuracy
Solution Approach 2:
The validation process applies different levels of scrutiny to different results based on their incongruence level. Highly incongruent results undergo full web search validation, while consistent results are accepted without additional verification. This localized quality approach ensures high accuracy for problematic results while avoiding time waste on already-validated consistent outputs
Data Source
AI summary
Embodiments relate to automatically providing multi-cloud generative artificial intelligence (AI) arbitrage, orchestration, and accuracy validation. An aspect includes inputting a user prompt to artificial intelligence (AI) models to obtain results and in response to receiving the results from the AI models, determining that at least one incongruent result is found in the results. An aspect includes resolving an issue of the at least one incongruent result in the results, in response to resolving the issue, merging the results to obtain a final result, and presenting the final result.