Connor LaPorte

Detail-oriented AI generalist with hands-on experience in data training, prompt engineering, and quality evaluation across diverse AI systems. Skilled in analyzing model outputs, improving accuracy through iterative feedback, and working with varied data formats. Complemented by a strong academic foundation in cybersecurity, including projects focused on digital forensics, network analysis, and risk assessment. Brings a structured, analytical approach to improving both AI performance and system security.

Improved AI response accuracy and consistency by evaluating and refining hundreds of model outputs using structured quality guidelines and feedback loops.

Designed and optimized prompts across diverse tasks, increasing successful instruction following and reducing common model errors through iterative testing.

Conducted digital forensics investigations on simulated systems, analyzing disk images and recovering artifacts to identify potential security incidents

Performed network security assessments by identifying vulnerabilities and recommending mitigation strategies based on traffic analysis and threat modeling