Evals for AI Products — from model.fit() to market.fit()

This module establishes the scientific and conceptual foundations of evaluation. Learners examine why evaluation governs reliability, trust, and the iterative refinement of AI systems. We explore traditional predictive evaluation, generative evaluation, and agentic evaluation frameworks, along with uncertainty measurement, dataset splits, and error sources such as noise, bias, and variance. The module sets the baseline reasoning habits needed to design meaningful evaluation strategies across any model type, domain, or product context.

1. 1. Why Eval Matters in AI Development
2. 2. Types of Evaluation
3. 3. Bias, Variance, and Noise
4. 4. Dataset Splits and Leakage
5. 5. Uncertainty & Confidence Measures

This module deepens evaluation skills for classical supervised and unsupervised learning. Learners study metrics for classification, regression, clustering, time-series forecasting, and probabilistic models. Beyond rote formulas, the module emphasizes interpretability, edge cases, error slice analysis, and when traditional metrics fail—especially under real-world conditions or distributional drift. Understanding foundational metrics provides the scaffolding on which more complex LLM and agent evaluation approaches build.

1. 1. Evaluating Classification Models
2. 2. Evaluating Regression Models
3. 3. Evaluating Clustering & Unsupervised Models
4. 4. Evaluating Time-Series Models
5. 5. Evaluating Probabilistic & Bayesian Models

This module trains learners to dissect model errors using systematic, scientific debugging techniques. Rather than rely solely on summary metrics, learners perform micro-level and slice-level error analysis, identifying patterns in residuals, subgroup behaviors, adversarial weaknesses, and distributional anomalies. Diagnostic skills form the backbone of both classical ML and modern LLM product engineering, enabling teams to isolate root causes and design targeted improvements.

1. 1. Residual and Statistical Error Analysis
2. 2. Slice and Subgroup Evaluation
3. 3. Counterfactual and Perturbation Testing
4. 4. Adversarial and Robustness Evaluation
5. 5. Drift, Shift, and Data Quality Diagnostics

This module explores the evaluation of generative and foundation models, with special attention to LLMs. Learners examine benchmark design, LLM-as-a-judge strategies, prompt-based evaluation, chain-of-thought assessment, multilingual robustness, and noise sensitivity. Modern LLM evaluation is subtle; learners practice methods that avoid biased scoring, capture reasoning quality, and ensure comparisons are meaningful and repeatable.

1. 1. Challenges of Open-Ended Output Evaluation
2. 2. Static Benchmarks for LLMs
3. 3. LLM-as-a-Judge
4. 4. Prompt-Based and Chain-of-Thought Evaluation
5. 5. Robustness Tests for LLMs

This module covers evaluation of agentic systems—LLM agents that plan, use tools, call APIs, and execute multi-step workflows. Learners examine retrieval-augmented generation (RAG), pipeline debugging, error propagation, and tool-call correctness. These systems are complex and require composite evals that test reasoning, planning, grounding, and tool-use reliability simultaneously.

1. 1. Evaluating Retrieval-Augmented Generation (RAG)
2. 2. Evaluating Multi-Step Pipelines
3. 3. Evaluating Tool-Use and APIs
4. 4. Evaluating Planning and Memory
5. 5. Simulation-Based Evaluations

This module focuses on evaluating multimodal AI systems that integrate text, images, audio, and video. Learners study detection and segmentation metrics, audio accuracy metrics like WER, and temporal coherence metrics for video. The module emphasizes evaluating not just single modalities but cross-modal consistency, grounding, alignment, and fidelity across complex inputs.

1. 1. Evaluation of Vision Models
2. 2. Evaluation of Audio & Speech Models
3. 3. Evaluation of Video Models
4. 4. Cross-Modal Alignment

Evaluation is only as strong as the data behind it. This module covers dataset design, annotation workflows, synthetic data generation, benchmark creation, dataset maintenance, and on-policy evaluation data for LLM agents. Learners understand how high-quality evaluation datasets unlock systematic discovery of failure modes, bias sources, and edge cases.

1. 1. Designing Evaluation Datasets
2. 2. Annotation Workflows and Quality Control
3. 3. Synthetic Data for Evaluation
4. 4. Benchmark Curation & Maintenance
5. 5. On-Policy Eval Sets for Agents

This module teaches how to adapt evaluation methods to regulated industries and high-stakes applications. Learners explore healthcare, finance, legal, enterprise search, education, and other verticals. Domain-specific evaluation blends technical metrics with regulatory, human, and economic requirements.

1. 1. Healthcare Evaluation
2. 2. Finance Evaluation
3. 3. Legal & Compliance Evaluation
4. 4. Enterprise Search & Productivity Evaluation
5. 5. Education & Mastery Evaluation

This module focuses on evaluating AI safety, alignment, fairness, and harm prevention. Learners examine red-teaming, jailbreak detection, subgroup fairness, privacy leakage, guardrail testing, and harmful content evaluation. Safety evaluation is essential for enterprise, regulated, and consumer-facing systems.

1. 1. Harm Detection & Red-Teaming
2. 2. Jailbreak & Vulnerability Evaluation
3. 3. Fairness & Subgroup Evaluation
4. 4. Privacy & Leakage Evaluation
5. 5. Guardrail and Safety Policy Evaluation

This module teaches evaluation in real-world production settings. Learners study monitoring, drift detection, online evaluation strategies, canary deployments, shadow testing, and CI/CD integration. Operational evaluation ensures a model remains reliable long after initial offline performance appears strong.

1. 1. Monitoring & Observability
2. 2. Drift Detection & Response
3. 3. AB Testing & Online Evaluation
4. 4. Shadow & Canary Deployments
5. 5. Continuous Evaluation in CI/CD

This module focuses on evaluating latency, throughput, compute efficiency, token consumption, caching behavior, speculative decoding, and cost-quality tradeoffs. Learners quantify the operational economics of large models and learn how to drive down cost while maintaining or improving quality.

1. 1. Latency & Throughput Evaluation
2. 2. Token Efficiency & Decoding Strategies
3. 3. Compute & Resource Efficiency
4. 4. Cost-Quality Frontier Evaluation

This module evaluates AI not just as a model but as a business asset. Learners map evaluation metrics to commercial outcomes, measure uplift, quantify user experience improvements, model revenue impact, and estimate ROI for deployments across consumer and enterprise use-cases.

1. 1. Linking Evaluation to Business Metrics
2. 2. Uplift Modeling & Incremental Value Testing
3. 3. User Trust & Satisfaction Evaluation
4. 4. Case Studies Across Verticals
5. 5. AI Economics & ROI

This module teaches learners to design, operationalize, and maintain comprehensive evaluation suites. It covers planning, stakeholder alignment, scientific validity, experiment reproducibility, and the avoidance of common eval pitfalls. Learners build playbooks that scale across engineering teams and evolving product needs.

1. 1. Designing Evaluation Plans
2. 2. Experimentation & Scientific Validity
3. 3. Avoiding Evaluation Fallacies
4. 4. Evaluation Playbooks & Templates
5. 5. Cross-Team and Stakeholder Collaboration

The capstone module combines the entire course into a hands-on evaluation build. Learners design, implement, and defend a full evaluation framework for a real or simulated AI system. They present findings to peer reviewers, iterate through critique, and learn to justify evaluation choices scientifically and commercially.

1. 1. Designing the End-to-End Eval Suite
2. 2. Implementing the Evaluation Pipeline
3. 3. Peer Review & Rubric-Based Assessment
4. 4. Presenting Evaluation Results