ByteByteGo – AI Evals in Practice: For Engineers & PMs

A practical course dedicated to evaluating the quality, reliability, and safety of artificial intelligence, large language models (LLM), and autonomous AI agents

What you’ll learn

• Understand how AI evals work
• Learn why unit tests and traditional QA break on non-deterministic systems
• Measure AI quality like accuracy, safety, reliability, cost, and latency
• Turn real failures into your first evals with error analysis
• Take a multi-step agent trace and find where it went wrong in minutes, not days.
• Build LLM judges you trust
• Grade outputs automatically with LLM-as-a-Judge
• Write pass/fail rubrics that behave like assertions
• Validate every judge against human review before relying on it
• Red-team agents before attackers do
• Attack your own agent with prompt injection and jailbreaks
• Automate red teaming with Promptfoo
• Add guardrails that block attacks without blocking real users
• Put your agent through a safety and adversarial review your security team takes seriously.
• Build meaningful eval datasets
• Build golden datasets from real traffic plus validated synthetic data
• Version datasets like code so results stay reproducible
• Score RAG pipelines with the RAGAS metrics
• Evaluate agents on different dimensions
• Test whether agents call the right tools with the right arguments
• Judge the agent’s full trajectory and final answer
• Catch failures hiding in multi-agent handoffs
• Run evals in production
• Wire evals into CI/CD so regressions never merge
• Monitor live traffic
• Catch model and data drift
• Ship a complete eval system
• Take a capstone from untested agent to gated, monitored pipeline
• Pick the cheapest model that still meets your quality bar
• Turn every incident into a permanent regression test