The Address Was Real. It Just Wasn’t Mine.
Why AI needs more than a prompt to keep your business facts straight
I was building a document with Gemini meant to hold everything an AI tool needs to know about my business before it writes a word of copy or a line of schema markup. Name, categories, service area, address. I’d started a rough version and asked it to expand things into proper JSON-LD, the structured data sitting underneath the visible page that helps search engines understand what the site says.
What came back looked right. Business name, correct. Categories, correct. Price range, correct. Address: 6440 N Campbell Ave, Suite 210, Tucson, AZ 85718.
That’s not my address. It’s Vivace, a Northern Italian restaurant in the Catalina Foothills, established in 1993, and to the best of my knowledge has no relationship whatsoever to my photography business.
I caught it in about four seconds because I know where my mail goes, and this wasn’t it. Gemini apologized and corrected it, the way it apologizes for anything once you catch it, which isn’t the same as being trustworthy. I’d handed it a task that sat just past what I could verify myself on sight: structured data, schema fields, the layer I understand in principle but can’t proofread the way I proofread a sentence. That’s the bot-sitting territory from the last issue. The tools go furthest wrong precisely where you’re least equipped to catch them.
I only caught this one because the address happened to fall inside what I know cold.
I’ve been running this business through AI tools for more than a year now, and I still don’t trust Gemini with anything I can’t verify myself. I don’t know that changes.
Nothing published, no real damage. But I kept thinking about the version that doesn’t get caught in four seconds. I recognized my own address wasn’t the one in front of me. What happens with a fact I’d recognize a little less certainly? A credential date that’s off by a year. A service category that used to be right and quietly stopped being right somewhere along the way?
I built two documents for future work. Then I asked an AI tool to hold both of them against pages I’d already published.
What the Card Actually Is
The first document is what I’ve started calling my entity card. It gives an AI tool one place to check who I am instead of reconstructing the answer every time.
Legal name, founding date, the two cities I moved through before Tucson, the credentials with the years they were actually earned, the service categories in the language I’ve decided wins over the language that doesn’t. None of the information is secret. Most of it is already sitting in my ORCID record for anyone who wants to look.
None of it is new, either. Putting it in one dated document gives me something concrete to check everything else against, rather than asking an AI tool to reconstruct me from whatever it half-remembers every time I need something written.
A model without a document like this doesn’t lie to you, exactly. It encounters a gap and supplies something plausible enough to fit, and a business address in a business’s own city is plausible right up until it turns out to belong to someone else’s restaurant.
Give it a dated source of truth, and at least the address no longer depends on a guess.
Who It’s For, Not What I Sell
The second document took longer to admit I needed because it’s less obviously about me. It’s a set of profiles, one for each real kind of client or organization I work with, built in detail and in writing before I ask an AI tool to help with anything that touches marketing.
The shape is worth describing because the shape is the useful part.
One profile starts with the obvious markers: age range, income bracket, whether they rent or own. None of that does the actual work. What matters is three lines down: what they’re worried about before they ever email me, and which detail in my first response would tell them I already understand that worry without my having to say so out loud.
Demographics tell an AI tool who might buy something. Decision drivers tell it what might move that specific person from considering the service to contacting me. Most client profiles stop after the first part.
The same principle applies across the rest of my practice. A corporate team needs different language than an individual replacing a ten-year-old LinkedIn photo. A private pet client needs something different from a rescue trying to get an overlooked dog adopted. The subject might still be a dog, but the audience, purpose, emotional stakes, and useful next step are not the same.
Whatever I ask an AI tool to write, from a corporate service page to a caption for a shelter dog’s Instagram post, it’s supposed to know which situation it’s writing for and who needs to read it. Most of the time I don’t have to rebuild that context from scratch. It’s already in the reference material the tool is working from.
The Part I’d Already Solved
There’s a third piece of this that I wrote about a few issues back, the writing guide that tells whatever tool I’m using how I actually sound, not how a generic photographer sounds.
I won’t re-walk that ground here. By the time I built the other two documents, I already had proof this approach worked because the writing guide had been quietly doing its job for months.
The newer documents answer two different questions: is this aimed at the right person, and is it even true?
Checking the Work That Already Existed
I assumed the main benefit would be retrieval. Having the facts and the audience written down meant I could stop re-explaining myself every session. That part’s real, and it’s most of what I wrote about a few issues back.
Then I stopped treating the documents only as background material living in a knowledge library. I asked an AI tool to check my existing work against them.
I picked three older pages on my site, ones I hadn’t touched in a while, and asked whether they still matched what the entity card and the writing guide actually say. They didn’t, in two different ways.
One page, built for corporate clients, still sells something called an Executive Session. That’s the word I ruled out months ago, in writing, after my search data showed that people find me for “corporate team headshots,” not “executive headshots.” I didn’t deliberately put it back. The page was built before I’d made that decision, and I hadn’t gone back to check it against a decision that came later.
The tool caught it in one pass because for the first time it had something concrete to check against instead of an instinct that something read a little off.
The other page, a fine art series from my years in Kentucky, gives two different date ranges for when the work was made, one in the metadata and a different one in the paragraph underneath it. Read past that, and it describes the choice behind the work as something I made “before relocating west in 2011,” which skips the fact that I’d already left Kentucky for Washington three years earlier.
Claude helped me build that page. It was created before I had any of these persistent identity documents in place, which meant the tool was working from whatever context I supplied during that session and whatever it could infer around the edges.
I didn’t set out to invent anything. I’m sure Claude didn’t make that choice either. A compressed sentence had turned into an error in my biography, sitting on my own website, waiting for whatever eventually reads it and decides it’s true.
Both pages are getting rewritten this week. That’s not really the point.
Neither error would have surfaced from a normal proofread because a normal proofread checks whether the sentence works. What caught these was checking the sentence against something written down and dated, the same discipline that caught the restaurant’s address before it ever went live.
I got lucky with Gemini because I happened to know my own address cold. I don’t know every fact about my own business that well, and neither does any AI tool I hand a task to unless I’ve already told it what to check against.
The address was obvious. The next error might not be.