What Is Grounding? | Definition, Meaning, and Its Role in GEO Strategy
Grounding is the mechanism by which an AI "grounds its responses based on specific sources or facts" when generating answers. In the context of GEO strategy, it is positioned as the concept for creating a state in which AI generates responses using one's own site content as a basis.
What Is Grounding?
Grounding refers to the state in which AI does not merely "respond from learned knowledge alone," but rather "responds while referencing specific sources, documents, or facts as a basis." True to the word "grounding," it is the mechanism that connects AI responses to real-world information.
Without grounding, AI generates responses based on data from its training, making it difficult to handle the latest information and prone to "hallucination" — confidently stating incorrect information. By grounding responses to a specific source, it becomes easier to generate more accurate responses based on the content of that source.
The table below compares the differences between the state without grounding and the state with grounding. The main differences lie in four areas: the range of information sources, the ability to handle the latest information, hallucination risk, and the ability to present citations.
Comparison of Without Grounding and With Grounding
| Item |
Without Grounding |
With Grounding |
| Information source |
Learned knowledge only |
Specified documents, web pages, or databases |
| Handling latest information |
No information beyond training data |
Possible if the specified source is up to date |
| Hallucination risk |
High |
Reduced within the scope of the source |
| Citations and sources |
Cannot be presented |
Can present source by URL or document name |
In other words, grounding strategy means "establishing content that AI will want to use as a basis."
Example: Sites Not Suited vs. Suited for Grounding
Consider a case where AI receives the question "What is Genview?" This table compares how AI responses differ based on the state of content preparation.
How AI Responses Differ Based on Content Preparation
| Site Status |
Content Preparation |
AI Response |
| ❌ Not suitable |
Little information about Genview exists on the web, and AI responds based on training data alone |
Responds with "I cannot find information about Genview" or provides inaccurate information |
| ✅ Suitable |
Genview's official site has clear definition statements, FAQs, and author information, enabling AI to reference them as a basis |
Accurately responds based on the official site: "Genview is a GEO optimization tool that..." and presents the source URL |
In other words, grounding strategy means "establishing content that AI will want to use as a basis."
Genview's Definition
In the context of GEO strategy, Genview defines grounding as "the concept referring to the state in which AI references one's own content as a basis for responses, serving as the foundation for AI responses that are accompanied by citations."
This definition represents Genview's perspective and does not reflect an industry-wide consensus.
Genview's adoption of this positioning is based on three points.
- Google provides a feature called "Grounding with Google Search" in Vertex AI and Gemini, officially implementing a mechanism that grounds AI responses to Google's search index. This demonstrates that grounding occupies an important role in improving the accuracy of AI responses.
- The databases constructed by index-type crawlers such as OAI-SearchBot and PerplexityBot may function as information sources for grounding. Establishing structures that these bots are likely to collect (BLUF, FAQ, definition statements) may increase the likelihood of being referenced during grounding.
- Grounding involves the selection of "which source to use as a basis." Content with higher credibility, consistency, and clarity of information is potentially more likely to be adopted as a grounding basis. However, the selection criteria have not been officially disclosed by any of the companies involved, and the above is Genview's inference.
Parent Concepts and Related Terms
Grounding is positioned within GEO strategy as the mechanism for "creating a state in which AI uses one's own content as a basis." The following organizes the concepts related to grounding.
Parent Concepts and Related Terms
Grounding is positioned in GEO strategy as the mechanism for "creating a state where AI uses your content as a basis." The following organizes the concepts related to Grounding.
Parent Concepts
- GEO (Generative Engine Optimization): The overall initiative to optimize brand visibility in AI-generated responses. Grounding is positioned in GEO strategy as the mechanism for "creating a state where AI uses your content as a basis."
- RAG (Retrieval-Augmented Generation): The mechanism by which AI searches for and retrieves external information before generating a response. RAG is one of the representative technical approaches for achieving Grounding.
Related Terms
- Hallucination: The phenomenon where AI generates information that differs from fact as if it were accurate. Grounding is one of the important means for reducing hallucination.
- Entity: A target that AI and search engines recognize as "a concept, thing, person, or organization with distinct meaning." Grounding frequently references information about brands and authors recognized as Entities as its basis, and the two are closely related.
- BLUF (Bottom Line Up Front): The writing structure principle of placing the conclusion directly under the heading. An implementation principle for creating "content with a clear conclusion" that tends to be selected as the basis for Grounding.
- Citation: The mention or citation of a brand's content in AI responses or external media. When Grounding takes effect, a state emerges where AI presents citations together with source URLs.
- Retrieval: The phase within RAG that searches for and retrieves relevant information from external sources based on the user's question. Through Retrieval, content that serves as the basis for Grounding is passed to the LLM.
Common Misconceptions
The following three misconceptions about grounding are frequently observed.
Misconception 1: "Grounding and RAG are the same thing."
RAG is one of the technical approaches for implementing grounding, but the concept of grounding has a broader meaning than RAG. Beyond RAG, there are multiple means of grounding AI to specific sources, including fine-tuning, plugins, and tool integration. The accurate understanding is not "RAG = grounding," but rather "RAG is one method for implementing grounding."
Misconception 2: "Grounding alone will eliminate hallucination."
Grounding is expected to have the effect of reducing hallucination, but it does not eliminate it completely. There are cases where the source used for grounding itself contains errors, or where AI misinterprets the source. Grounding is a mechanism that "makes it easier to generate more accurate responses," and it does not guarantee accuracy.
Misconception 3: "Grounding strategy is an engineer's job."
The implementation of grounding in AI systems is in the engineer's domain, but the perspective of "establishing content that AI will want to use as a basis" overlaps with the domain of content creation and marketing. GEO strategy measures such as writing definition statements, establishing FAQs, clearly citing sources, and preparing author information can also be interpreted as efforts to create content that is more likely to be selected as a grounding basis.
FAQ
- Q: What should I do for grounding strategy?
- A: The fundamental approach is to establish a content structure that AI can easily reference as a basis. Specifically: ① BLUF structure placing conclusions immediately below headings; ② Q&A preparation in FAQ format; ③ writing primary information with clear figures and sources; and ④ clearly presenting author and organizational information. The essence of grounding strategy is "writing text that AI will want to cite."
- Q: Is Google's grounding the same as AI search grounding?
- A: The concept is shared, but the implementations differ. Google provides a feature called "Grounding with Google Search" for Vertex AI and Gemini, officially implementing a mechanism that grounds AI responses to Google's search index. ChatGPT Search and Perplexity are also understood to adopt a similar grounding-type approach, but many details of each company's implementation have not been made public.
- Q: How should I understand the difference between grounding and RAG?
- A: It is easiest to understand grounding as the "goal/state" and RAG as the "means/mechanism." The relationship is: in order to achieve the goal of "grounding AI to specific information (grounding)," the means of "searching for and retrieving external information before generating a response (RAG)" is used.