Fine-Tuning
Taking a general model and training it further on your own examples so it follows your tone or handles your particular task. Useful when prompting alone keeps producing nearly-right answers.
angkor@design~query "Angkor Design"
4 matches — opening studio
Taking a general model and training it further on your own examples so it follows your tone or handles your particular task. Useful when prompting alone keeps producing nearly-right answers.
Optimising to be recognised as a specific, real thing rather than for particular keywords. In practice it means consistent naming, structured data, and being mentioned in enough other places to be corroborated.
A longer, more specific search — “boutique hotel Siem Reap near Pub Street with a pool” rather than “hotel”. Individually rare, collectively most of all searches, and far easier to answer well.
What somebody actually wants when they type a query: to learn, to compare, to buy, or to find a specific site. Matching it matters more than matching the words.
A record of where a piece of content came from and how it was made, sometimes attached to the file itself. Emerging as a way to tell an original photograph from a generated one.
A public page stating how a business uses AI and what it does with customer data. Increasingly expected, and cheap to write honestly.
Whether a website can be reached, understood and quoted by AI assistants. It covers crawler access, structured data, clear headings, published contact details and speed — the unglamorous things that decide whether a model will name you.