Conversational AI Data Annotation & Quality Review
TextClassification
Worked on annotating and reviewing conversational datasets used to train AI assistants. Tasks included labeling user intent, tagging entities, evaluating response quality, and refining outputs to better match natural human tone and context. I handled large volumes of text data while following strict annotation guidelines and maintaining consistency across edge cases.
I also focused on quality control—flagging unclear prompts, correcting inconsistencies, and improving dataset clarity to make it more useful for training. The work required both attention to detail and strong judgment, especially when dealing with nuanced or ambiguous inputs. Overall, I helped improve the accuracy, coherence, and human-likeness of AI-generated responses.
Worked on annotating and reviewing conversational datasets used to train AI assistants. Tasks included labeling user intent, tagging entities, evaluating response quality, and refining outputs to better match natural human tone and context. I handled large volumes of text data while following strict annotation guidelines and maintaining consistency across edge cases. I also focused on quality control—flagging unclear prompts, correcting inconsistencies, and improving dataset clarity to make it more useful for training. The work required both attention to detail and strong judgment, especially when dealing with nuanced or ambiguous inputs. Overall, I helped improve the accuracy, coherence, and human-likeness of AI-generated responses.
2024 - Present