LLM Auditing and Evaluation (Edge Case & Mathematical Reasoning)
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Audited LLM outputs for logical consistency, focusing on reasoning errors and mathematical hallucinations. Evaluated edge cases to identify AI limitations in real-world operational and engineering scenarios. Implemented corrections by integrating physical and financial variables into theoretical model assessments.
• Conducted in-depth evaluation and feedback for LLM-generated responses
• Specialized in detecting and documenting cases of AI mathematical inaccuracies
• Integrated real-world, domain-specific knowledge to refine AI model outputs
• Utilized advanced Excel modeling and internal proprietary tools to document findings.
Audited LLM outputs for logical consistency, focusing on reasoning errors and mathematical hallucinations. Evaluated edge cases to identify AI limitations in real-world operational and engineering scenarios. Implemented corrections by integrating physical and financial variables into theoretical model assessments. • Conducted in-depth evaluation and feedback for LLM-generated responses • Specialized in detecting and documenting cases of AI mathematical inaccuracies • Integrated real-world, domain-specific knowledge to refine AI model outputs • Utilized advanced Excel modeling and internal proprietary tools to document findings.
2023 - Present