From an Information Retrieval perspective, queries are typically classified in several key ways: ## By Search Intent (Most Common) - **Informational** - Seeking knowledge/facts ("what is machine learning") - **Navigational** - Finding a specific site/page ("Facebook login") - **Transactional** - Wanting to complete an action ("buy running shoes") - **Commercial Investigation** - Research before purchasing ("best laptops 2025") Search intents shift as users travel [[customer-life-cycle]] ## By Query Length - **Head queries** - 1-2 words, high frequency ("weather", "news") - **Torso queries** - 3-5 words, medium frequency ("best pizza NYC") - **Long-tail queries** - 6+ words, low frequency but high specificity ## By Information Need Complexity - **Lookup/Factual** - Simple fact retrieval ("capital of France") - **Interpretive** - Requiring analysis ("why did the market crash") - **Exploratory** - Open-ended discovery ("trends in renewable energy") - **Comparative** - Evaluating options ("iPhone vs Samsung") ## By Specificity - **Broad/Ambiguous** - Multiple possible interpretations ("apple") - **Specific/Targeted** - Clear, narrow intent ("iPhone 15 Pro Max price") ## By Structure - **Keyword-based** - Traditional search terms - **Natural language** - Conversational queries ("How do I fix my wifi?") - **Boolean** - Using operators (AND, OR, NOT) **For AI/generative search context**: The distinction between **lookup vs. interpretive queries** is particularly relevant, as AI systems excel at interpretive queries where traditional search struggled, while simple lookup queries may not need the complexity of generative responses.