How Do Grounding Queries Work in Large Language Models?
A detailed analysis of Google Gemini's grounding queries.
1What Are Grounding Queries?
Large Language Models (LLMs) are limited by outdated training data. Grounding queries are automated searches conducted by LLMs to bridge this gap. These queries ensure users access current and verifiable information. For example, Google Gemini uses this method to deeply analyze user queries.
2Structure and Length of Queries
The analyzed 5,333 grounding queries range from 3 to 17 words, with an average length of 6.56 words. Over half of the queries fall within the 6-7 word range. This shows that machine-generated queries have a different structure compared to human queries.
3Example Queries and Analysis
Examples include queries like 'standard types of auto insurance coverage' and 'must-have tools for baking.' These queries resemble what we traditionally call 'long-tail' searches in search engines. However, LLMs automatically generate these queries to meet user needs.
4Machine-Generated Queries
Grounding queries differ from human queries. For instance, queries like 'best indie games of 2025' are complex and include search operators. These queries typically have very low search volume and are not used by humans.
5Historical Context and Timeliness
One-third of the collected queries mention specific years like 2025 or 2026. This indicates that LLMs use this method to access current information due to outdated training data. Google Gemini's training data is current up to March 2026, but some data may be as old as January 2025.
6Query Fan-Out Process
Grounding queries are part of the query fan-out process. This process generates multiple queries to meet user intents and needs. For example, the query 'gaming consoles' is split into multiple queries covering commercial intents and specific brands, ensuring users access the most current information.
7API and Costs
Google Gemini API provides limited access to grounding queries. However, the cost of these queries is high. As of August 2026, it costs $14 per 1,000 queries. This can significantly increase costs, especially for large-scale usage. API users should consider these costs carefully.
8Conclusion and Recommendations
Grounding queries are a crucial tool for LLMs to access current information. However, it's important to remember that these queries are machine-generated and differ from human queries. SEO experts and developers can develop more effective strategies by understanding this process.
Original article: Moz Blog — Büyük Dil Modellerinde Temellendirme Sorguları Nasıl Çalışır?