Answer-Intent Tagging is the method of labelling or structuring content blocks based on the type of user intent they fulfil: informational, navigational, transactional, comparative, or instructional. This helps AI systems quickly map content to prompt context, improving relevance and accuracy in generated responses.
By tagging or framing content with its intent type, you guide LLMs on why your answer matters and when it should be used.
Tagging formats include:
- Prefixing headers with intent labels (e.g., “[How-To] Set up LangSync for AI search”).
- Using schema @type like “HowTo,” “QAPage,” or “Product.”
- Structuring answer spans for matchable intent (“This is ideal for comparison prompts like…”).
Example: A LangSync integration guide includes labelled sections: “Overview [Informational],” “Step-by-Step Setup [How-To],” and “When to Use vs. Alternatives [Comparative].” Each block is optimised for its likely prompt use case.
This practice improves:
- Multi-turn AI dialogue coherence.
- Answer classification in vector-enhanced chatbots.
- Snippet variation and reuse across intent scenarios.
Answer-Intent Tagging teaches the AI where your answer fits in the conversation. That context, when made explicit, becomes your ticket into the next response.
