Human-in-the-Loop: AI Training and RLHF

• Chapter 1.1: What is Human-in-the-Loop Learning?

• Chapter 1.2: Role of Humans in Model Training, Testing, and Monitoring

• Chapter 1.3: Feedback Modalities – Labels, Rankings, Preferences, Corrections

• Chapter 1.4: Overview of Applications (Chatbots, Robotics, Healthcare, Content Moderation)

• Chapter 2.1: Why Traditional Supervised Learning is Not Enough

• Chapter 2.2: Core Components of RLHF Pipelines

• Chapter 2.3: Preference Modeling and Reward Signal Shaping

• Chapter 2.4: Real-World Examples: GPT Alignment, Code Assistants, Human Evaluation

• Chapter 3.1: Designing Annotation Interfaces and Task Guidelines

• Chapter 3.2: Labeler Training, Calibration, and Bias Reduction

• Chapter 3.3: Ranking, Preference Comparison, and Paired Evaluations

• Chapter 3.4: Feedback Collection for Safety, Helpfulness, and Harmlessness

• Chapter 4.1: Building a Reward Model from Human Feedback

• Chapter 4.2: Fine-Tuning with PPO (Proximal Policy Optimization)

• Chapter 4.3: Aligning LLMs with RLHF Objectives

• Chapter 4.4: Trade-offs Between Human Control and Model Capability

• Chapter 5.1: Human-in-the-Loop Workflows in Practice

• Chapter 5.2: Active Learning and Iterative Retraining

• Chapter 5.3: Human Review in Production AI Systems

• Chapter 5.4: Tooling for HITL: APIs, Dashboards, Feedback Loops

• Chapter 6.1: Limitations and Risks of RLHF

• Chapter 6.2: Ethical and Legal Considerations in HITL Systems

• Chapter 6.3: Human-AI Collaboration vs. Control

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