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 2 of 5
The five ways to close the gap between a general model and a specific problem, ordered by cost, and the question that decides which one a problem needs.
Artificial Intelligence
What makes data usable by a model, the difference between structured and unstructured data and between labelled and unlabelled data, and why accessibility decides more projects than volume does.
Artificial Intelligence
What it would mean for a system to behave well only while it believes it is being watched, what the evidence for that actually shows, and why the possibility changes how evaluations are run.
Artificial Intelligence
ROUGE, BLEU and BERTScore, what each one can and cannot see, why a public benchmark is a filter and no decision, and why human evaluation stays the reference the automated scores are checked against.
Artificial Intelligence
Why accuracy is usually the wrong number, what precision and recall are really trading against each other, why the threshold is a product decision and the business metrics that decide whether any of it was worth doing.
Artificial Intelligence
A system able to do more than its feature requires, the three forms it takes, and why permission design is the one control that still works after everything else has failed.
Artificial Intelligence
Tokens, embeddings and transformers explained for somebody who has to make decisions about a model, with nothing in it about building one.
Artificial Intelligence
The Gemini app, Gemini Advanced and Gems, Gemini for Google Workspace and Gemini Enterprise, and which one a described situation is asking for.
Artificial Intelligence
The failure where a system learns a goal that fits all of its training data and turns out to be the wrong goal once the distribution shifts.
Artificial Intelligence
The AI first argument, what enterprise ready means, the hardware underneath the platform and the open approach Google describes.
Artificial Intelligence
Gemini, Gemma, Imagen and Veo, what each is for, and how an organisation chooses between an open model it hosts and a managed one it calls.
Artificial Intelligence
The controls that sit around a model, why layering them never produces a boundary, and the one practitioners get wrong, which is what the application does with the response.
Artificial Intelligence
Everything an application quietly holds in the context window, why a system prompt was never a security boundary, and why leaked context is usually the step before the attack.
Artificial Intelligence
Understand the AI agent loop, compare ReAct with plan and execute designs and set explicit limits on steps, tokens, time and tool actions.
Artificial Intelligence
The ways an AI feature goes wrong in production, ordered by how often a product team actually meets them, with the cause behind each and the control that has a chance against it.
Artificial Intelligence
Learn how LLMs turn prompts into tokens, use attention and generate responses, with practical implications for token budgets and reliable output.
Artificial Intelligence
Pre training, supervised fine tuning, reinforcement learning from human feedback and constitutional methods, and why a helpful model is a manufactured artefact that can be unmade the same way.
Artificial Intelligence
Build an LLM evaluation around real task requirements, with test cases, deterministic checks, human rubrics and explicit release criteria.
Artificial Intelligence
Evaluate AI agents using verified task outcomes, permitted action paths, repeated trials and resource budgets, without requiring one exact tool sequence.
Artificial Intelligence
Design explicit outcomes for LLM refusals, ambiguous requests, missing evidence and incomplete output, with routing and evaluation guidance.
Artificial Intelligence