LLM evaluation and guardrails course
AI apps fail silently: a prompt change that looks fine can break a hundred answers. Module 9 teaches the evaluation and security engineering that catches this before users do.
Module 9 has 11 lessons and about 24 guided hours. It sits between RAG (Module 8) and agents (Module 10), because agents that act need the strongest testing.
Each lesson, each module and the full program earn a certificate of completion with a public verification ID. It is a certificate of completion, not a degree.
Every module unlocks in order, after the one before it.
From your first line of Python to production LLM apps, RAG, agents, evals and fine-tuning. Reverse-engineered from 2026 AI-engineer job descriptions.
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Automated tests for AI features: a fixed set of inputs run on every change, scored by code checks and calibrated LLM judges, so regressions are caught before release.
Checks around a model that screen inputs, limit what tools can do, require human approval for risky actions and validate or redact outputs.
Yes. Module 9 covers the OWASP Top 10 for LLM apps, including prompt injection, data leakage and excessive agency, and you red-team your own app.
An evaluation suite and red-team report for the Module 8 RAG assistant, wired into CI so a regression fails the build.
AI vs ML vs DL vs GenAI: deep notes, a hands-on lab and a quiz. No card. No catch.