1.7 Data Annotation Fundamentals

Study Time: 2.5 hours Prerequisites: Module 1.1 Learning Objectives:

• Understand the connection between data annotation and AI evaluation
• Master image annotation types (bounding boxes, polygons, segmentation, keypoints)
• Apply text annotation techniques (NER, classification, sentiment, intent)
• Handle audio and video annotation tasks
• Meet annotation quality standards (IoU, IAA, error rate)

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Introduction

Many platforms (Mercor, DataAnnotation.tech, Scale AI) use the same talent pool for both annotation and evaluation work. Understanding annotation fundamentals makes you a more versatile and valuable contributor.

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1.7.1 Annotation vs. Evaluation: How They Connect

Aspect	Data Annotation	AI Evaluation
What you do	Label raw data to train AI models	Judge AI outputs to improve behavior
Input	Images, text, audio, video (unlabeled)	AI-generated responses to prompts
Output	Labels, bounding boxes, classifications	Ratings, rankings, justifications
Key skill	Following labeling guidelines precisely	Applying rubrics consistently
Quality metric	Inter-annotator agreement, accuracy	Agreement with gold standards

Transferable Skills: Attention to detail, guideline adherence, consistency, documentation, these matter equally in both domains.

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