AI Evals: From Theory to Production
A practical framework for evaluating LLM applications. Go beyond simple accuracy metrics to build robust, reliable, and business-aligned AI systems.
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 Overview
A practical, end-to-end framework for evaluating LLM applications. Build robust, reliable, and business-aligned AI systems.
What Evals Actually Are
Evals are a structured process for measuring whether AI products behave as intended. They are distinct from unit tests, benchmarks, or PRDs. Instead, they connect messy reality to a repeatable improvement process through a 5-step loop:
Examine real conversations
Review 40-100 system traces to identify the first upstream error per conversation
Document observations
Write specific, product-aware notes (e.g., 'Should have handed off to a human' not 'janky')
Group into failure modes
Organize into 4-7 actionable categories with a domain expert maintaining consistency
Build automated checks
Pure code for simple checks (format, tool calls), LLM-as-judge only for semantic decisions
Operationalize and monitor
Run in CI and production sampling, track trends by category, prioritize by business risk
The M.A.G.I. Framework
The course is built around the M.A.G.I. framework, a production-tested approach to AI evaluation with four pillars:
Metrics
QAG, G-Eval, Contextual Precision/Recall, Tool Correctness. Select 5 or fewer metrics per use case.
Automation
CI/CD integration with offline and online scoring. Target under 5 minutes per eval execution.
Governance
Metric ownership, quarterly reviews, golden-dataset versioning. Keep your eval framework trustworthy.
Improvement
Full-funnel tracing, threshold tuning, continuous feedback loops. Data-driven refinement cycles.
Real-World Case Studies
Learn from production failures that drove the creation of this evaluation framework:
The Coffee Machine Reimbursement Trap
RAG FailuresHigh RiskA retrieval system fetched permissive policy language while omitting exclusion details. High faithfulness with wrong context is dangerous.
Faithfulness 95% | Contextual Recall 45% | Contextual Precision 60%
The Outdated PTO Policy Nightmare
Temporal & Data IssuesBusiness CriticalUsers received guidance based on obsolete policies despite current information existing in the knowledge base under poor metadata.
Temporal Accuracy 25% | Contextual Precision 30% | 23 User Complaints
Agent Tool Hallucination Crisis
Agent ReliabilitySystem CriticalProduction agents generated calls to non-existent tools or malformed parameters, triggering system failures and emergency rollback.
Tool Correctness 70% | Task Completion 45% | System Uptime 82%
Free Resources on EvalMaster
What Evals Actually Are
The 5-step loop methodology
M.A.G.I. Framework
Measure, Automate, Govern, Improve
Architecture Patterns
8 patterns from Simple RAG to Multi-Agent
Metric Catalog
16 eval metrics with benchmarks
Case Studies
Production failure analysis
LangGraph Agents
Agent orchestration patterns
Implementation Guide
Step-by-step setup
Integrations
LangChain, LlamaIndex, Langfuse
Course Modules
8 self-paced modules, each with a hands-on assignment, followed by a capstone project. Learn at your own speed.
Module 1
Foundations & Lifecycle
Anchor on business goals and set up the foundational plumbing for evaluation.
- 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 to pre-prod gates to prod monitoring to continuous improvement
- Minimal instrumentation: traces, spans, session IDs, prompt/tool logs
Deliverable: Baseline PRD with metrics map, tracing enabled on a sample application, and a one-page evals plan
Module 2
Systematic Error Analysis
You can't measure what you haven't named. Learn to turn raw failures into an actionable taxonomy.
- Sampling strategies for error analysis (real traces vs. synthetic data)
- Open-coding techniques to identify root errors and axial coding to group them
- Basic quantitative analysis of qualitative data (pivot counts, severity, risk ranking)
- Common anti-patterns: vague labels, committee thrash, overfitting
Deliverable: Error log from labeling 40-100 traces, a v1 failure taxonomy, and a prioritized Top 5 Failure Modes document
Module 3
Evaluators That Stick
Convert your taxonomy into automated checks that can run at scale.
- Designing deterministic checks: schema/JSON validity, required fields, tool-call presence, latency/cost thresholds
- Designing semantic checks (LLM-as-judge) for judgment calls ensuring binary outputs
- Best practices for test dataset organization and versioning
Deliverable: An evaluation runner (CLI, notebook, or CI job) that executes both code-based checks and 1-2 LLM judges against a test dataset
Module 4
Alignment & Collaboration
Ensure your automated judges are trustworthy and aligned with human judgment.
- Inter-annotator agreement (IAA) basics to de-bias rubrics
- Using a confusion matrix over raw accuracy to understand and reduce false positives and false negatives
- Implementing a simple governance loop for proposing, reviewing, and accepting changes to evaluators
Deliverable: A confusion matrix comparing an LLM judge to human labels, an alignment write-up, and a change-control checklist
Module 5
Architecture-Specific Strategies
Apply targeted evaluation techniques to the architectures that matter most.
- 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: A targeted test suite for one architecture pattern (RAG or Tool Use) with pass/fail thresholds
Module 6
Production Monitoring
Move evaluations from notebooks into CI/CD and live production monitoring.
- 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: A functioning CI gate that blocks merges on eval failure, plus a production sampling config
Module 7
Human Review Workflows
Design efficient human-in-the-loop processes that scale.
- 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: A human review workflow spec with sampling strategy, rubric, and feedback integration plan
Module 8
Cost Optimization
Ship quality without burning budget. Optimize the cost-performance frontier.
- 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: A cost optimization plan with measured baselines and projected savings
Capstone
Capstone Project
Select one real-world agent workflow and build a production-grade evaluation program:
- Refined failure taxonomy with data-backed prioritization
- Automated pipeline with 3+ deterministic checks and 2+ LLM judges
- Confusion matrix and performance thresholds
- Architecture-specific test suite
- Functioning CI gate and production dashboard
- Documented cost optimization plan
Ready to master AI evals?
Join the waitlist to be notified when enrollment opens. Early bird pricing available to waitlist members.
Join the waitlist