AI Search Evaluation & Content Rating Project
Worked on AI-driven search evaluation tasks within the music and entertainment domain, focusing on assessing the relevance, quality, and alignment of search results with user intent. Evaluated content based on multiple factors including accuracy, popularity, contextual fit, and thematic similarity (such as genre, mood, and tone), using structured rating guidelines. Applied consistent evaluation standards to distinguish between varying levels of result quality (e.g., perfect vs good relevance), ensuring objectivity and accuracy across tasks. Identified irrelevant or mismatched results, inconsistencies, and gaps in alignment with user queries, while providing clear, guideline based feedback to improve overall output quality. Maintained high attention to detail and consistency while working independently across large volumes of evaluation tasks in a remote environment.