AI Failure Pattern Research — Independent Study
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Independently researched and documented how large language models (LLMs) fail in real-world use. Created adversarial prompts to systematically identify and reproduce five distinct failure patterns in AI model responses. Compiled findings and presented them as a structured research report.
• Explored LLM vulnerabilities such as Commitment Cascade and Context Window Drift
• Developed adversarial prompt sets for systematic model evaluation
• Categorized failure modes to support more robust AI evaluation protocols
• Reported results as a foundation for further prompt engineering or annotation work
Independently researched and documented how large language models (LLMs) fail in real-world use. Created adversarial prompts to systematically identify and reproduce five distinct failure patterns in AI model responses. Compiled findings and presented them as a structured research report. • Explored LLM vulnerabilities such as Commitment Cascade and Context Window Drift • Developed adversarial prompt sets for systematic model evaluation • Categorized failure modes to support more robust AI evaluation protocols • Reported results as a foundation for further prompt engineering or annotation work
2024 - Present