AI Fundamentals
What AI, ML and GenAI are — and where each fits in real work.
An adaptive test across 12 core AI skills. You finish with a skill map and a study plan linked straight to lessons and labs.
What AI, ML and GenAI are — and where each fits in real work.
Next-token prediction, training vs inference, and why models hallucinate.
Tokens, context windows and what fits in a prompt.
Prompt anatomy, few-shot examples and step-by-step reasoning.
Reliable JSON and schemas from model output.
Vectors, similarity and semantic search.
Chunking, retrieval and grounded answers with citations.
Function calling, tool schemas and validating arguments.
The agent loop, MCP, multi-agent pipelines and memory.
Test sets, rubrics and LLM-as-judge to measure quality.
Prompt injection, PII, bias and hallucination risk.
Caching, model choice and token budgets.