Tshepo Malesela

AI model evaluation is the systematic process of assessing an artificial intelligence or machine learning model's performance, reliability, and suitability for its intended task. It involves comparing a model's predictions against a "ground truth" (known correct outcomes) using various quantitative metrics and qualitative assessments.

Specialised in designing and refining high-precision instructions (prompts) and managing the broader 'information ecosystem' (context) that guides Large Language Models (LLMs). I focus on reducing hallucinations and improving output reliability by implementing advanced techniques like Chain-of-Thought and RAG (Retrieval-Augmented Generation). My approach ensures AI agents have the right data at the right time to perform complex, multi-step tasks with professional-grade accuracy.