Structured Data Testing
Checking that your markup is valid and says what you meant. Worth doing after every template change, because broken structured data usually fails silently rather than visibly.
angkor@design~query "Angkor Design"
4 matches — opening studio
Checking that your markup is valid and says what you meant. Worth doing after every template change, because broken structured data usually fails silently rather than visibly.
How well a system can show why it reached a decision. It matters most where the decision affects somebody — a loan, a diagnosis, a job application — and “the model said so” is not an answer.
The limits placed on what an AI may say or do — refusing certain requests, staying on topic, never inventing a price. Essential on anything customer-facing.
A system that suggests what somebody might want next based on behaviour — the “customers also bought” of an online shop. One of the oldest commercial uses of machine learning.
Giving an AI real source material to answer from, instead of letting it rely on memory. A grounded answer can be checked and cited; an ungrounded one is where hallucinations come from.
Perplexity’s crawler. Perplexity cites its sources prominently, so being readable by it is one of the more direct routes to a visible citation.
A plain-text way of writing formatted documents using symbols like # for a heading. AI systems read it easily because the structure is unambiguous, which is why some sites now offer it alongside HTML.