Inference
The moment an AI actually answers, as opposed to when it was trained. Training happens once and costs a fortune; inference happens every time somebody asks, and is what you pay for per use.
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The moment an AI actually answers, as opposed to when it was trained. Training happens once and costs a fortune; inference happens every time somebody asks, and is what you pay for per use.
Structured data describing the business itself: name, logo, address, contact details, social profiles. The single most useful markup for being recognised, and the one most small sites are missing.
Splitting a long page into smaller passages so an AI can retrieve just the relevant part. Pages with clear headings chunk cleanly; a single unbroken wall of text does not.
Using historical data to estimate what will happen next: which customers will lapse, which stock will run out. Useful precisely to the degree that the past resembles the future.
Another attribute-based markup standard for embedding machine-readable facts in a page. Less common on business websites than JSON-LD, and largely equivalent in what it can express.
A working model of something real — a building, a supply chain — kept in step with it using live data, so changes can be tested before they are made for real.
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.