ByteByteGo - AI Evals in Practice For Engineers & PMs

What you'll learn

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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

Homepage

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