Emma: The Adversarial System
Meet Emma, Mavera's adversarial quality assurance system. Learn how this sophisticated AI works alongside Ellie to catch errors, identify missing information, and ensure users receive comprehensive an
Emma is Mavera's sophisticated adversarial system, designed to work in tandem with Ellie to ensure the highest quality and accuracy of outputs provided to users. As Ellie's counterpart, Emma's primary role is to catch mistakes, identify missing information, and guarantee comprehensive and precise responses.
Key Features
Output Review: Emma meticulously reviews all outputs generated by Ellie using specifically fine-tuned LLMs and transformer models.
Error Detection: Employs boolean Machine Learning algorithms for initial output classification and specialized LLMs for detailed error analysis.
Feedback Loop: Provides detailed feedback to Ellie about identified issues, fostering continuous improvement.
Quality Assurance: Orders outputs to be redone if significant gaps are detected before they reach the user.
Continuous Learning: Emma is continuously trained on user jobs, enhancing her ability to catch errors and provide valuable feedback over time.
How Emma Works
When Ellie generates an output, Emma reviews it using her specialized AI models.
Emma first runs boolean ML algorithms to classify the output as good (true) or bad (false).
If flagged as false, Emma's highly specialized LLMs expand upon the issues and identify what Ellie missed.
Emma informs Ellie about the gaps and specifies which frameworks (Gremlins, Sprites, or Personas) should be called back to address the missing information.
For significant issues, Emma orders a complete redo of the output before it reaches the user.
Throughout this process, Emma continuously learns from user jobs, improving her error detection and feedback capabilities.
Benefits
Enhanced Accuracy: Emma's adversarial approach ensures that outputs are thoroughly vetted for accuracy and completeness.
Continuous Improvement: The feedback loop between Emma and Ellie leads to ongoing enhancements in output quality.
Comprehensive Responses: By identifying and filling information gaps, Emma ensures users receive complete and thorough answers.
Quality Assurance: Emma acts as a final quality check, significantly reducing the chance of erroneous or incomplete information reaching users.
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