The vocabulary the exams and the daily work both assume you already have. Every entry gives the definition, the source it is drawn from, and the misreadings that follow from getting it wrong.
94 concepts in Artificial Intelligence, page 4 of 5
What a model retains from its training data, how that can be pulled back out, and what differential privacy does and costs.
Artificial Intelligence
Learn when multiple AI agents help, how to design subtask contracts and how to measure handoff quality, shared budgets and coordination costs.
Artificial Intelligence
The spectrum from a closed hosted model to a fully open release, the arguments on both sides, and what changes when a team builds on weights it can download.
Artificial Intelligence
Why people accept output they should have checked, why it gets worse as the system gets better, and what a product can do about it.
Artificial Intelligence
Build a repeatable prompt and context engineering workflow with versioned artefacts, evaluation ownership, trace review and a practical capstone project.
Artificial Intelligence
Run a practical Python prompt engineering lab, compare two prompts by case and segment, then test your own changes with an optional live model.
Artificial Intelligence
Understand direct and indirect prompt injection, how untrusted content redirects LLM applications and how to test the boundaries around data and tools.
Artificial Intelligence
Build prompt regression tests that compare baseline and candidate versions, expose segment level failures and enforce quality, cost and latency gates.
Artificial Intelligence
Version prompts with their model, schema and retrieval settings, then deploy through evaluation, canary monitoring and a tested rollback process.
Artificial Intelligence
Deliberately trying to make a system misbehave before somebody else does, how the exercise is run, what it cannot establish and what regulators now expect of it.
Artificial Intelligence
Bias, fairness, robustness, safety and veracity treated as things that can be measured and acted on, plus the legal exposure that arrives with generative output.
Artificial Intelligence
Why every accuracy figure belongs to the data it was measured on, and what happens to a deployed model as the world drifts away from that data.
Artificial Intelligence
The problem of supervising a system on work a person cannot check, the research directions aimed at it, and the control available in the meantime.
Artificial Intelligence
Why capability improves predictably with size while individual abilities appear abruptly, and what that does to the job of testing a model.
Artificial Intelligence
Security across the machine learning lifecycle, what Google's Secure AI Framework is for and what it became, and the Google Cloud tools the Gen AI Leader exam expects a candidate to name.
Artificial Intelligence
Access control, encryption and the shared responsibility model applied to machine learning, plus the attack surfaces that only exist because there is a model in the path.
Artificial Intelligence
Two ways a system does exactly what it was trained to do and still fails, and why that is harder to fix than disobedience would be.
Artificial Intelligence
The three ways a model learns, what each one needs from the data, and how to tell which problem is actually in hand.
Artificial Intelligence
What a team is actually depending on when it ships an AI feature, how those dependencies fail, and the provenance work that has to happen while it is still boring.
Artificial Intelligence
Two questions hiding inside one word, whether the objective specified was the right one and whether the trained system actually acquired it.
Artificial Intelligence