Training AI with Humans Course

Lesson 1 • Human Roles in AI Pipelines

Maps the distinct human functions across a typical AI development pipeline. Clarifies how each role affects downstream model behavior.

Lesson 2 • Types of Human Feedback Signals

Surveys the main feedback formats humans provide to AI systems. Grounds abstract feedback types in concrete annotation examples.

Lesson 3 • What AI Training Actually Means

Defines machine learning training as an iterative data-driven process. Connects the concept to the human roles that make it function.

Lesson 1 • Primary vs. Secondary Data Sources

Distinguishes data generated directly for training from repurposed existing data. Evaluates trade-offs in cost, control, and representativeness.

Lesson 2 • Defining Data Requirements

Translates a model's intended task into concrete data specifications. Prevents costly misalignment between collected data and training goals.

Lesson 3 • Ethical and Legal Considerations in Sourcing

Identifies consent, privacy, and intellectual property obligations when collecting data. Applies responsible sourcing principles to avoid downstream harm.

Lesson 4 • Sampling Strategies for Representativeness

Applies statistical sampling methods to ensure training data reflects real-world distributions. Reduces systematic gaps that cause model failure.

Lesson 1 • Writing Effective Annotation Guidelines

Teaches structured guideline writing that minimizes annotator ambiguity. Directly impacts inter-annotator agreement and label reliability.

Lesson 2 • Anatomy of an Annotation Task

Breaks down every component of a well-formed annotation task. Connects task structure to the consistency and usability of resulting labels.

Lesson 3 • Inter-Annotator Agreement Metrics

Introduces quantitative measures of label consistency across multiple annotators. Enables teams to detect and resolve systematic disagreements.

Lesson 4 • Pilot Testing and Guideline Validation

Runs small-scale annotation pilots to surface guideline failures before full deployment. Reduces rework costs and improves final label quality.

Lesson 1 • Annotator Recruitment and Onboarding

Defines criteria for selecting annotators with the right skills and background. Structures onboarding to reduce early errors and attrition.

Lesson 2 • Workflow Optimization and Throughput

Applies process improvement techniques to reduce annotation cycle time. Balances speed with quality to meet project deadlines.

Lesson 3 • Annotator Performance Monitoring

Tracks individual annotator metrics to identify underperformance and bias patterns. Enables targeted coaching and fair performance management.

Lesson 4 • Annotation Platform Selection

Evaluates tooling options against task type, scale, and team structure. Connects platform capabilities to workflow efficiency and data security.

Lesson 1 • Training and Evaluating Reward Models

Converts preference labels into a reward model that scores outputs. Covers training procedures and evaluation metrics specific to reward modeling.

Lesson 2 • Evaluating RLHF-Trained Models

Assesses alignment quality using human evaluation and automated proxies. Connects evaluation results back to pipeline improvements.

Lesson 3 • Policy Optimization with Human Feedback

Uses the trained reward model to fine-tune a generative policy via RL. Addresses stability challenges and KL divergence constraints.

Lesson 4 • Collecting Human Preference Data

Designs comparison tasks where humans rank or choose between model outputs. Produces the preference dataset that trains the reward model.

Lesson 1 • Ethical Review and Governance Processes

Establishes review boards, checklists, and escalation paths for ethical oversight. Embeds accountability into the annotation and training workflow.

Lesson 2 • Sources of Bias in Human-Labeled Data

Catalogs cognitive, demographic, and process-level biases that enter labels. Connects each bias type to specific downstream model failures.

Lesson 3 • Bias Auditing Techniques

Applies systematic auditing methods to detect bias in datasets and model outputs. Produces actionable audit reports for remediation.

Lesson 4 • Mitigation Strategies for Training Data

Implements data-level interventions to reduce bias before model training begins. Evaluates effectiveness of each strategy against fairness targets.

Lesson 1 • Evaluating Active Learning Efficiency

Measures the annotation cost savings and model quality gains from active learning. Provides metrics to justify and optimize the active learning strategy.

Lesson 2 • Designing Human-AI Annotation Loops

Structures iterative cycles where model predictions and human corrections alternate. Reduces total annotation volume needed to reach quality targets.

Lesson 3 • Selecting Informative Samples for Labeling

Applies selection algorithms to surface the highest-value unlabeled examples. Directly reduces annotation budget without sacrificing model performance.

Lesson 1 • Model Monitoring and Drift Detection

Monitors deployed models for performance degradation and distribution shift. Connects monitoring alerts to human review and retraining actions.

Lesson 2 • Versioning Models and Datasets

Applies version control practices to both datasets and trained models. Enables rollback, reproducibility, and systematic improvement tracking.

Lesson 3 • Production Feedback Loop Design

Captures real-user interactions and errors to generate ongoing training signal. Closes the loop between deployed model behavior and retraining.

Lesson 4 • Scaling Annotation Operations

Addresses organizational and technical challenges of growing annotation teams. Maintains quality standards as volume and complexity increase.