Prompt engineering

Prompt engineering sounds technical but is mostly editorial judgement: telling the model who it's speaking for, what it must not do, and what good output looks like. Event teams meet it in two places. First, inside products — your event assistant's behaviour is shaped by system prompts the vendor wrote, which is why one chatbot politely declines off-topic questions while another cheerfully speculates about competitor shows. Second, in daily work — teams that maintain shared, tested prompts for session descriptions, exhibitor emails, and social copy get consistent output; teams where everyone freestyles get twenty tones of voice. The commercial angle is mundane but real: prompt quality decides whether AI saves your marketing team hours or generates drafts that take longer to fix than writing from scratch. The common mistake is writing a prompt once and never revisiting it — good prompts are iterated against real outputs, with failure cases folded back into the instructions. One honest nuance: prompt engineering has limits vendors don't advertise. No instruction reliably stops a model from making things up under pressure; prompts shape behaviour, they don't guarantee it. Anything customer-facing still needs guardrails and testing, not just a well-written instruction.