Loganix

"Grounding" Doesn't Mean What You Think It Means 🗺️ Words matter, especially when they're quietly reshaping how an entire industry thinks. "Grounding" comes from "ground truth," rooted in statistics and originally cartography, where it literally meant going outside to verify that your map matched reality. In some AI models, "ground truth" is the objectively correct real-world data, like sensor readings or medical records, used to anchor the model to reality. Not documents. Not web pages. Reality. The core problem with LLMs is that there's no ground truth signal during training or generation. The model isn't checking its answer against the facts; it's only predicting the next most likely word. What Microsoft, a company I deeply respect + admire, calls "grounding" is actually RAG (Retrieval-Augmented Generation): retrieving web documents to supplement a response. Useful! But web text is written by humans, about reality, not reality itself. Those documents can be wrong, biased, SEO-manipulated, or outdated. RAG is better-informed guessing. True "grounding" is fundamentally a different thing. The uncomfortable part: Microsoft's own AI Guide features a quote from me where, after significant pushback on their "grounding" framing during a long interview, I said: "RAG does help the LLM ground its response in information from the web, but it's worth remembering that not everything online is true." The caveat got published. The correction didn't & the term has escaped into GEO AIO E-I-E-I-O gauntlet. I've since watched real people repeat versions of Microsoft's definition & treat it as fact. And I don't blame them. They're trying to keep up with all of these changes. Microsoft's new "Grounding Queries" metric in Bing Webmaster Tools makes this even more confusing. Those aren't user queries. They're background searches AI quietly generates when a user submits a prompt. For example, when you ask "should I bring an umbrella in Seattle?" the AI might internally generate "Seattle weather today" to inform its response. Calling those "grounding queries" buries an already-misused term one layer deeper. I raised this concern with Microsoft & suggested alternatives like "Retrieval Queries" or "AI Queries," which I feel would be more accurate and less confusing but to no avail. The real irony? Microsoft employs SO many world-class AI researchers. They know the difference. By rebranding RAG and synthetic AI queries as "grounding," a precise technical term has now become a marketing buzzword. SEOs are now optimizing for a word we don't have a shared definition of. And when AI researchers hear you use "grounding" this way, it'll erode your credibility. As AI continues to reshape industries, it's more important than ever for us to understand these nuances. By learning the true meaning behind AI terms & tech we can communicate more effectively, make better decisions & drive real results. 19 Days until the next Actionable AI For Marketers Course 🎓