Azure AI Fundamentals and Machine learning

Prompt engineering is the practice of composing what you send a generative model, the instruction, the supporting material and the worked examples, so that it returns something you can use.

Retrieval-augmented generation is a pattern that searches a body of documents when a question arrives and places the passages it finds into the model's prompt, so the answer is drawn from those sources rather than from training alone.

Natural language processing is the area of AI that makes human language readable and writable by machine: sorting text into categories, pulling structured facts out of it, translating it, generating it and measuring whether any of that came out right.

A data warehouse is a database built for analysis rather than for running the business: it holds copies of records from many operational systems, integrated under one set of definitions, kept as history rather than overwritten, and arranged so that questions across years and departments run fast.

A data lake is a store that keeps data in its original form, files of any shape in cheap object storage, loaded before anyone has decided what questions will be asked of it, so that the structure is applied when the data is read rather than when it is written.

Business intelligence is the practice and the tooling for turning an organisation's data into reports and dashboards that people use to run it: connecting to the sources, modelling the data into measures everyone agrees on, and presenting it so that a manager sees the state of the business without asking an analyst.