Evaluating Large Language Model Outputs: A Practical Guide Course

Lesson 1 • How LLMs Generate Text

Covers token prediction, temperature, and sampling at a conceptual level. Explains why these mechanisms produce the failure modes evaluators must detect.

Lesson 2 • Core Quality Dimensions of LLM Output

Introduces the primary axes—accuracy, fluency, relevance, coherence, and safety—used to judge any LLM response. Provides the taxonomy that all later chapters apply.

Lesson 3 • The Evaluator Mindset

Builds the critical, skeptical stance required for rigorous evaluation. Distinguishes surface impressiveness from substantive quality to prevent common evaluator errors.

Lesson 1 • Writing Precise Evaluation Criteria

Teaches operationalisation: converting vague goals into observable, testable statements. Directly enables reliable scoring in subsequent chapters.

Lesson 2 • Versioning and Maintaining Criteria

Addresses how criteria must evolve as models, tasks, and business needs change. Establishes governance practices that keep evaluation frameworks current and auditable.

Lesson 3 • Building Scoring Rubrics

Guides construction of multi-level rubrics with anchor descriptions at each score point. Rubrics built here are used throughout the course for hands-on practice.

Lesson 4 • Aligning Criteria with Business Goals

Connects evaluation design to organisational objectives such as accuracy, brand voice, and compliance. Ensures criteria reflect real-world success, not just technical quality.

Lesson 1 • Measuring Inter-Rater Reliability

Introduces Cohen's kappa, Krippendorff's alpha, and percent agreement as reliability metrics. Teaches learners to diagnose and resolve disagreement among raters.

Lesson 2 • Pairwise and Absolute Rating Approaches

Contrasts side-by-side comparison with independent absolute scoring, detailing when each is appropriate. Learners select the right approach for a given evaluation scenario.

Lesson 3 • Rating Protocol Design

Covers the sequence, interface, and instructions that guide human raters through an evaluation task. Well-designed protocols reduce cognitive load and rating drift.

Lesson 4 • Rater Training and Calibration

Details how to onboard raters using gold-standard examples and calibration sessions. Calibrated raters produce the reliable data that downstream analysis depends on.

Lesson 1 • Selecting and Combining Metrics

Provides a decision framework for choosing metric combinations that cover multiple quality dimensions. Learners design metric suites tailored to specific tasks and organisational needs.

Lesson 2 • Interpreting and Misinterpreting Metrics

Addresses Goodhart's Law, metric gaming, and correlation gaps between automated scores and human judgment. Builds critical literacy so learners avoid over-relying on single metrics.

Lesson 3 • Reference-Free and Model-Based Metrics

Covers perplexity, learned quality estimators, and LLM-as-judge approaches that require no gold reference. Expands the evaluator's toolkit for open-ended generation tasks.

Lesson 4 • Reference-Based Metrics Overview

Explains BLEU, ROUGE, METEOR, and BERTScore as metrics that compare outputs to gold references. Establishes when reference availability makes these metrics appropriate.

Lesson 1 • Reporting and Tracking Factual Errors

Establishes structured error logging, severity classification, and feedback loops to model developers. Transforms individual findings into systemic improvement data.

Lesson 2 • Automated Hallucination Detection Tools

Surveys retrieval-augmented verification, natural language inference models, and specialised fact-checking APIs. Enables scalable detection beyond what manual review alone can achieve.

Lesson 3 • Manual Fact-Checking Workflows

Teaches claim decomposition, source triangulation, and structured verification checklists for human evaluators. Provides a repeatable process applicable to any domain.

Lesson 4 • Domain-Specific Verification Strategies

Adapts verification approaches for high-stakes domains such as medicine, law, and finance where errors carry serious consequences. Calibrates rigour to domain risk level.

Lesson 1 • Policy and Content Guideline Evaluation

Teaches evaluators to map organisational content policies to observable output characteristics. Ensures evaluation enforces real policy rather than vague notions of appropriateness.

Lesson 2 • Red-Teaming and Adversarial Testing

Introduces structured adversarial probing to surface harmful outputs that standard prompts do not elicit. Red-teaming findings directly inform safety evaluation criteria.

Lesson 3 • Defining Harm in LLM Outputs

Establishes a harm taxonomy covering physical, psychological, societal, and reputational categories. A precise taxonomy is prerequisite to consistent detection and reporting.

Lesson 4 • Building a Safety Evaluation Pipeline

Integrates automated classifiers, human review, and escalation paths into a repeatable safety workflow. Produces a pipeline learners can adapt to their organisational context.

Lesson 1 • Communicating Study Findings

Teaches structured reporting of evaluation results to technical and non-technical audiences. Clear communication ensures findings translate into organisational action.

Lesson 2 • Experimental Design for Model Comparison

Applies A/B testing, within-subject designs, and controlled variable isolation to model comparison studies. Rigorous design prevents confounds from invalidating conclusions.

Lesson 3 • Statistical Analysis of Evaluation Results

Introduces significance testing, confidence intervals, and effect size reporting for evaluation data. Learners distinguish meaningful differences from noise in metric scores.

Lesson 4 • Sampling and Dataset Construction

Covers stratified sampling, edge-case inclusion, and dataset size estimation for reliable evaluation. A well-constructed dataset is the foundation of any valid study.

Lesson 1 • Scaling Evaluation with Crowdsourcing

Addresses platform selection, task design, quality control, and cost management for large-scale crowdsourced annotation. Enables evaluation volume that internal teams cannot achieve alone.

Lesson 2 • Evaluation System Architecture

Maps the components of a production evaluation system: data ingestion, scoring, storage, and reporting. Provides a blueprint learners adapt to their technical environment.

Lesson 3 • Continuous Monitoring and Drift Detection

Establishes metric dashboards, alerting thresholds, and drift detection methods for ongoing quality assurance. Prevents silent degradation of model output quality over time.

Lesson 4 • Human-in-the-Loop Integration

Designs workflows that route outputs to human reviewers based on automated confidence thresholds. Balances cost and quality by reserving human effort for high-uncertainty cases.