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LLM Evaluation & Observability

Name: LLM Evaluation & Observability
Availability: InStock

Build the discipline every senior AI developer in 2026 needs: rigorous evaluation and production observability for LLM-powered systems. Learn eval frameworks, LLM-as-judge patterns, tracing with LangSmith and Langfuse, and cost + latency management. Ends with a capstone: build a complete eval harness for the customer support agent you built in the Building AI Agents course.

5 hours

31 lessons

Certificate included

Instructor

JP Instructor

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This course includes:

5 hours of comprehensive lessons
Knowledge-check quizzes in every module
Certificate of completion
Lifetime access to all content
Mobile-friendly responsive design

Course Curriculum

Module 1: Why Evaluation Matters

What LLM evaluation is, why it is the top hiring signal in 2026, the eval pyramid, and the anti-patterns that catch first-time teams.

6 lessons

What is Evaluation and Why It Matters More For LLMs

Preview

5 minutes

Preview Lesson

The Eval Pyramid: Unit, Regression, Observational, Red-Team

6 minutes

Common Eval Anti-Patterns

5 minutes

Setting a Baseline Before You Build

5 minutes

What Employers Actually Want to See

5 minutes

Module 1 Quiz: Why Evaluation Matters

10 minutes

Module 2: Building Your First Eval

The 2026 eval framework landscape, defining a dataset, choosing metrics, running end-to-end and iterating on results.

6 lessons

The 2026 Eval Framework Landscape

Preview

6 minutes

Preview Lesson

Defining an Evaluation Dataset

5 minutes

Choosing Metrics

5 minutes

Running Your First Eval End to End

5 minutes

Reading and Iterating on Results

5 minutes

Module 2 Quiz: Building Your First Eval

10 minutes

Module 3: LLM-as-Judge Patterns

When to use LLM-as-judge, writing rubrics that produce reliable judgments, the biases you must know about, cost management, and custom vs off-the-shelf judges.

6 lessons

When LLM-as-Judge Fits (and When It Doesn't)

Preview

5 minutes

Preview Lesson

Rubrics That Produce Reliable Judgments

6 minutes

Judge Biases and Mitigations

6 minutes

Judge Cost at Scale and Cheaper-Model Routing

5 minutes

Custom vs Off-the-Shelf Judges

5 minutes

Module 3 Quiz: LLM-as-Judge

10 minutes

Module 4: Tracing and Observability

Why tracing beats logging for LLM systems, the 2026 tracing landscape, instrumenting agents, reading traces to debug, and sampling strategies at scale.

6 lessons

Why Tracing Beats Logging for LLM Systems

Preview

5 minutes

Preview Lesson

The 2026 Tracing Landscape: LangSmith, Langfuse, OpenTelemetry

5 minutes

Instrumenting Agents: Calls, Tools and Decisions

6 minutes

Reading Traces to Debug Real Failures

5 minutes

Sampling and Retention at Scale

5 minutes

Module 4 Quiz: Tracing and Observability

10 minutes

Module 5: Cost, Latency and Production Health

Measuring cost and latency per request, token accounting and prompt caching, alerts and dashboards, SLO/SLI thinking for probabilistic services, catching quality regressions before users notice.

6 lessons

Measuring Cost and Latency Per Request

Preview

5 minutes

Preview Lesson

Token Accounting and Prompt Caching Patterns

5 minutes

Alerting and Dashboards for LLM Systems

5 minutes

SLO/SLI Thinking for Probabilistic Services

5 minutes

Regression Detection: Catching Quality Drops Before Users Notice

5 minutes

Module 5 Quiz: Cost, Latency and Production Health

10 minutes

Module 6: Capstone: Build an Eval Harness for the Support Agent

Take the customer support agent from the Building AI Agents capstone and wrap it in a full eval harness: dataset, judge rubric, tracing, cost + latency tracking and a written reflection. Portfolio-eligible.

1 lessons

Build an Eval Harness for the Support Agent

180 minutes

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