“LLM Response Quality Evaluation Framework”
I independently worked on a structured LLM response evaluation project to understand and practice AI output quality assessment. In this project, I evaluated 50+ AI-generated responses across different domains such as general knowledge, logical reasoning, and basic technical explanations. I created a simple scoring framework to assess responses based on: • Factual accuracy • Logical consistency • Instruction adherence • Clarity and coherence • Presence of hallucinations • Basic bias and safety considerations All responses were annotated in a structured spreadsheet format using predefined evaluation guidelines to maintain consistency. I also reviewed ambiguous and edge-case responses to improve scoring reliability and reduce subjective judgment differences. Through this project, I developed practical understanding of AI response validation, structured feedback generation, and core RLHF concepts.