AI Evals: From Theory to Production
Stop shipping AI that breaks in production. This course teaches you how to build evaluation systems that actually catch failures — before your users do.
Built by Nidhi Vichare from real production experience shipping LLM systems. No theory fluff — every lesson comes with code you can deploy Monday morning.
Who is this for?
ML/AI Engineers
Building agents, RAG pipelines, or LLM-powered features and tired of "it works on my machine" failures.
Tech Leads & Managers
Need to answer "how do we know this AI is working?" with data, not vibes.
Product Teams
Shipping AI products and want confidence that quality improves, not just "seems fine."
The curriculum covers the entire evaluation lifecycle: from reading traces and naming failure modes, to automated CI/CD quality gates, production monitoring with Langfuse, and human-in-the-loop review. You'll leave with a complete eval program you can deploy.
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.
Weekly Syllabus
Click any week to expand the full curriculum details.
Anchor on business goals and set up the foundational plumbing for evaluation.
Topics
- 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.”
You can’t measure what you haven’t named. Learn to turn raw failures into an actionable taxonomy.
Topics
- 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.
Convert your taxonomy into automated checks that can run at scale.
Topics
- 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.
Ensure your automated judges are trustworthy and aligned with human judgment.
Topics
- 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.
Apply targeted evaluation techniques to the architectures that matter most.
Topics
- RAG metrics: Contextual Precision, Recall, Faithfulness, chunk-level attribution
- Tool Use testing: correct tool selection, parameter accuracy, retry handling
- Multi-turn continuity: session-level coherence, state tracking across turns
- Designing architecture-aware test suites with edge cases
Deliverable: Assignment 5: A targeted test suite for one architecture pattern (RAG or Tool Use) with pass/fail thresholds.
Move evaluations from notebooks into CI/CD and live production monitoring.
Topics
- CI/CD integration: eval gates in GitHub Actions, pre-merge quality checks
- Safety guardrails: PII detection, toxicity filters, policy compliance
- Production tracking: real-time dashboards, alerting on metric drift
- Canary deployments and shadow scoring for safe rollouts
Deliverable: Assignment 6: A functioning CI gate that blocks merges on eval failure, plus a production sampling config.
Design efficient human-in-the-loop processes that scale.
Topics
- Strategic sampling: when and what to send to human reviewers
- Reviewer UX: annotation interfaces, rubric design, calibration sessions
- Feedback loops: routing human judgments back into golden datasets and judge tuning
- Measuring reviewer agreement and handling disagreements
Deliverable: Assignment 7: A human review workflow spec with sampling strategy, rubric, and feedback integration plan.
Ship quality without burning budget. Optimize the cost-performance frontier.
Topics
- Value mapping: which evaluations deliver the most signal per dollar
- Smart routing: model cascades, cached responses, selective evaluation
- Performance trade-offs: latency vs. quality vs. cost Pareto analysis
- Building a cost model and projecting savings at scale
Deliverable: Assignment 8: A cost optimization plan with measured baselines and projected savings.
Weeks 9-12: Capstone Project
Ship a complete evaluation program for a real-world agent workflow. Select one workflow (e.g., “customer support ticket routing,” “sales inquiry qualification”) and build a production-grade evaluation program around it.
Ready to stop guessing and start measuring?
Every week builds on the last. By week 12, you'll have a production eval program running in CI — not a slide deck. Includes all materials, 8 assignments, capstone project support, and certificate.
Want to explore before you commit?
The EvalMaster framework, case studies, metrics reference, and architecture guides are all free. Dive in and see how production evals actually work.
Part of evalmaster.nidhivichare.com — the complete playbook for production AI evaluation.