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GenAI Safety & Evaluation Engineering

L5-L6 · 7 courses · 102 chapters

Design automated LLM evaluation pipelines, red-team GenAI systems, build bias detection and fairness benchmarks, implement guardrails.

What you'll learn

Core responsibilities this discipline prepares you for.

Build automated evaluation pipelines

to continuously measure LLM output quality

Design evaluation harnesses with RAGAS, DeepEval, and NeMo Evaluator SDK for multi-metric scoring
Create evaluation datasets with ground-truth annotations and run cross-provider comparisons
Wire CI gates that automatically block deployments when faithfulness or relevance scores degrade

Conduct red-team exercises

— probe LLMs for vulnerabilities

Automate adversarial testing with Garak for prompt injection, jailbreak, and data extraction probes
Run multi-turn adversarial campaigns with Meta GOAT and DeepTeam for agent vulnerability testing
Execute red-team campaigns against realistic systems, discover vulnerabilities, and write actionable findings

Implement production guardrails

— content filters, PII detection, jailbreak prevention

Configure NeMo Guardrails with Colang policy language, Llama Guard 4, and Prompt Guard 2
Add Presidio for PII detection/redaction and Model Armor for Google-native content safety
Layer multiple defenses, test against comprehensive attack suites, and quantify safety-vs-helpfulness tradeoffs

Design GenAI governance frameworks

aligned with regulations

Map EU AI Act risk classification and implement NIST AI RMF control frameworks
Build OWASP LLM Top 10 mitigation strategies mapped to technical controls
Create governance artifacts, conduct risk assessments, and build automated audit trail pipelines

Evaluate GenAI agent behavior

— trajectory quality, tool selection accuracy

Build trajectory scoring systems measuring tool selection accuracy and task completion quality
Design human preference alignment tests and regression test suites for agent workflows
Evaluate multi-step agent executions to identify failure modes and build targeted regression tests

Monitor bias, fairness, and hallucination rates

in production

Detect bias across protected attributes using statistical fairness metrics and disparity analysis
Measure hallucination rates through ground-truth comparison and citation verification
Implement continuous bias scanning, hallucination detection, and alerting for metric drift

Build safety incident response processes

for deployed GenAI systems

Design safety monitoring dashboards with severity-based alert routing and escalation paths
Build incident triage workflows with containment procedures and post-incident reporting templates
Simulate safety incidents end-to-end and practice the full detection-to-resolution workflow

Design LlamaFirewall policies

for agent safety

Configure LlamaFirewall middleware for controlling agent tool access and output filtering rules
Set up multi-agent safety boundaries with policy-based execution constraints
Validate firewall policies against adversarial scenarios where agents attempt to bypass controls

Your learning path

7 courses · sequenced for compounding · 102 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Advanced16 Ch

Step 5

GenAI Agent Engineering

Build production-grade agents with hosted LLMs — planning, tools, memory, evaluation, and orchestration patterns.

Advanced14 Ch

Step 6

GenAI Evaluation, Safety & Governance

Evaluate, red-team, and govern GenAI systems — offline evals, online metrics, safety guardrails, compliance.

Advanced10 Ch

Capstone

GenAI Operations

Run GenAI in production — monitoring, dunning, incident response, cost control, and the on-call runbook.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

DeepEval

Unit-test LLM outputs in CI

Ragas

Measure RAG quality at scale

Arize Phoenix

Observe and debug LLM apps in labs

Guardrails AI

Declarative output validation

NeMo Guardrails

Programmable conversation rails

Llama Guard

Open-source content moderation

Presidio

PII detection + redaction in labs

Argilla

Human-in-the-loop eval datasets

OpenAI API

Baseline model for eval labs

Anthropic API

Claude for red-team reasoning labs

Langfuse

Trace eval runs end-to-end

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