GEO & AI Search

Prompt

A prompt is the instruction or question a user gives an AI system.

Prompts tend to be longer, more conversational, and more specific than traditional search queries, so "what's the best digital agency in Bangkok for B2B lead generation" replaces "Bangkok digital agency." Content that answers these fuller, intent-rich questions directly is better positioned to be surfaced in AI responses. Understanding how people phrase prompts informs both content and GEO strategy.

Prompts are the new query data, and they are invisible: no tool reports what people ask assistants. The practical substitutes are your own sales conversations, support tickets and chat logs, because prompts read like those, full sentences with context and constraints. Content built to answer a fully-specified situation, budget included, requirements included, is what gets retrieved when someone asks the machine a fully-specified question. The two-word-keyword habit optimises for an interface that a growing share of your audience no longer starts from.

Example

The search query was "crm software". The prompt is: "we're a 12-person Thai property agency on spreadsheets, need something under 1,000 baht per user with LINE integration, what should we use?" One vendor's comparison page answers that full scenario, budget tiers, LINE support, migration from spreadsheets, and gets cited by assistants; the vendor optimising for two-word queries never enters the conversation.

Frequently asked questions

How do prompts differ from search queries?
Prompts are longer, conversational and situation-rich: budgets, constraints and context included. They read like a request to a consultant, not a keyword. Content that answers full situations, not just terms, is what retrieval systems reach for.
How does understanding prompts help my content strategy?
Prompts mirror sales conversations, so your own enquiries, chats and support tickets are a corpus of them. Write pages that resolve those fully-specified situations, budget ranges, comparisons, constraints, and you match how a growing share of discovery now happens.

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