🎓 Professional Course

AI Evals: From Theory to Production

Master production-grade AI evaluation frameworks. Build robust, reliable, and business-aligned AI systems with comprehensive evaluation strategies that drive real impact.

12 weeks

Capstone Project

8 Assignments

Course Description

This course provides a practical, end-to-end framework for evaluating Large Language Model (LLM) applications. Moving beyond simple accuracy metrics, students will learn to build robust, reliable, and business-aligned AI systems. The curriculum covers the entire evaluation lifecycle: from initial development and instrumentation to automated pre-production gates, continuous production monitoring, and efficient human-in-the-loop review.

Learning Objectives

Business Alignment

Align AI evaluation strategies with core business goals and KPIs for measurable impact.

Systematic Error Analysis

Develop systematic processes for identifying, classifying, and prioritizing LLM failure modes.

Automated Evaluation

Build and validate automated evaluation pipelines using code-based checks and LLM-as-judge evaluators.

Production Integration

Integrate evaluations into CI/CD lifecycle to create robust quality gates and enable safe, continuous improvement.

Architecture-Specific Strategies

Implement specialized evaluation techniques for RAG and Tool Use architectures.

Cost Optimization

Analyze and optimize cost-performance trade-offs through intelligent routing and targeted evaluations.

Course Schedule & Topics

Week 1: Foundations & Lifecycle

Assignment 1

Focus: Anchor on business goals and set up the foundational plumbing for evaluation.

Topics Covered:

•How evals reduce risk and drive impact (aligning to KPIs like conversions, CSAT, cost)

•LLM-specific pitfalls (stochasticity, context dependence, tool/RAG failure modes)

•The evaluation lifecycle: dev → pre-prod gates → prod monitoring → continuous improvement

•Minimal instrumentation: traces, spans, session IDs, prompt/tool logs

Deliverable: Assignment 1: Baseline Product Requirements Document (PRD) + metrics map, tracing enabled on a sample application, and a one-page "evals plan."

Week 2: Systematic Error Analysis

Assignment 2

Focus: You can't measure what you haven't named. Learn to turn raw failures into an actionable taxonomy.

Topics Covered:

•Sampling strategies for error analysis (real traces vs. synthetic data)

•Open-coding techniques to identify root errors and axial coding to group them into a taxonomy

•Basic quantitative analysis of qualitative data (pivot counts, severity, risk ranking)

•Common anti-patterns: vague labels, committee thrash, overfitting

Deliverable: Assignment 2: An error log from labeling 40-100 traces, a v1 failure taxonomy, and a prioritized "Top 5 Failure Modes" document.

Week 3: Evaluators That Stick

Assignment 3

Focus: Convert your taxonomy into automated checks that can run at scale.

Topics Covered:

•Designing deterministic checks: schema/JSON validity, required fields, tool-call presence, latency/cost thresholds

•Designing semantic checks (LLM-as-judge) for judgment calls (e.g., politeness, groundedness), ensuring binary (TRUE/FALSE) outputs

•Best practices for test dataset organization and versioning

Deliverable: Assignment 3: An evaluation runner (CLI, notebook, or CI job) that executes both code-based checks and 1-2 LLM judges against a test dataset.

Week 4: Alignment & Collaboration

Assignment 4

Focus: Ensure your automated judges are trustworthy and aligned with human judgment.

Topics Covered:

•Inter-annotator agreement (IAA) basics to de-bias rubrics

•Using a confusion matrix over raw accuracy to understand and reduce false positives (FP) and false negatives (FN)

•Implementing a simple governance loop for proposing, reviewing, and accepting changes to evaluators

Deliverable: Assignment 4: A confusion matrix comparing an LLM judge to human labels, an alignment write-up, and a change-control checklist.

Weeks 5-8: Production-Ready Evaluation

Advanced topics covering architecture-specific strategies, production monitoring, human review processes, and cost optimization.

Week 5

Architecture-Specific Strategies

RAG metrics, Tool Use testing, Multi-turn continuity

Week 6

Production Monitoring

CI/CD integration, Safety guardrails, Production tracking

Week 7

Human Review Workflows

Strategic sampling, Reviewer UX, Feedback loops

Week 8

Cost Optimization

Value mapping, Smart routing, Performance trade-offs

Weeks 9-12: Capstone Project

Ship a complete evaluation program for a real-world agent workflow.

✓Taxonomy v2: Refined failure taxonomy with data-backed prioritization

✓Eval Pipeline: Automated pipeline with ≥3 deterministic checks and ≥2 LLM judges

✓Alignment Report: Confusion matrix and documented performance thresholds

✓Architecture-Specific Suite: Targeted test suite for workflow architecture

✓CI Gate & Dashboard: Functioning CI gate and production sampling dashboard

✓Cost Optimization Plan: Documented plan with measured/projected cost savings

Scope: Select one real-world agent workflow (e.g., "customer support ticket routing," "sales inquiry qualification") and build a complete evaluation program around it.

Course Fee

To Be Announced

Early bird pricing will be available to waitlist members. Pricing includes all course materials, assignments, capstone project support, and completion certificate.