A Simple Spreadsheet for Logging AI Answers About Your Cafe

Thomas Garner

Oct 7

11 min read

Short answer: a working log needs far fewer columns than most owners assume, and the format matters less than actually filling it in the same way every time. Date, tool, the exact prompt you typed, whether your cafe was named, whether the facts given were correct, which sources were cited, and a screenshot file name cover nearly everything worth tracking.

It is easy to have a vague, ongoing sense that ChatGPT or Google's AI answers get something wrong about your cafe, without ever pinning down what, when, or how often. A spreadsheet does not fix what those tools say. It turns a fuzzy impression into something you can actually look back on a month later and compare against what you remember, rather than relitigating the same half remembered complaint every few weeks without any actual record to check it against.

This post lays out a column structure modeled on the format Hillcane already uses for its own research log, along with a naming convention for screenshots and a short monthly reading routine, all described as tested rather than theoretical, with the honest limits of that test stated plainly. None of this requires special software. A spreadsheet tool most owners already have access to, a folder for screenshots, and a small amount of consistent discipline are the entire toolkit.

The columns that matter

The model for this log traces back to the structure Hillcane already uses in its own research work, a stored file that tracks city, query, tool, timestamp, the organic results returned, who showed up in the map results, whether a given cafe was named, and free text notes. That structure was not designed for a single owner tracking one cafe, but it holds up well once trimmed down to what a single business needs to track.

For an individual cafe owner, the practical version of that same idea comes down to date, time, which tool was used, the exact prompt typed in, whether a location setting was applied, whether the cafe was named in the answer, whether the facts given, hours, address, whether it is open, were correct, which sources if any were cited, a screenshot file name, and a short notes field for anything that does not fit the other columns.

That is nine columns, not dozens. The temptation with any tracking spreadsheet is to add more fields than anyone will fill in consistently, a rating scale, a sentiment score, a dropdown for every possible error type. In practice, a log with fewer columns that gets filled in every time beats an elaborate one that gets abandoned after two entries, because the value of this kind of log comes entirely from having entries to compare over time, not from how detailed any single entry is.

It helps to think of each column as answering one specific question rather than trying to capture everything at once. Date and time answer when. Tool answers which product gave this answer. The exact prompt answers what was asked, which matters because a slightly different phrasing can return a noticeably different answer from the same tool on the same day. Whether the cafe was named and whether the facts were correct are two separate columns rather than one combined judgment, since a tool can name a cafe correctly while still getting a detail wrong, or leave a cafe out entirely while everything it does say about competitors is accurate.

Keeping the location setting as its own column is worth calling out specifically. Many AI tools behave differently depending on whether a location is set explicitly in the prompt, detected automatically from a device, or left unset entirely, and an answer recorded under one location setting is not directly comparable to one recorded under another. Recording which setting was in effect for each entry keeps later comparisons honest rather than comparing answers that were never really asking the same question in the first place.

Testing it against real entries

A column list on paper is not the same as a column list that survives contact with actual use. Testing this structure against ten real entries, prompts run through an actual AI tool and logged as they came back, is what separates a workable format from one that looks fine until someone tries to use it.

Running through that test surfaced a practical point worth carrying forward: a few columns tend to sit empty more often than expected, particularly the sources cited field, since not every AI answer names where its information came from. Rather than treating an empty field as a failure of the log, it is worth simply noting when a column is left blank because the tool genuinely gave nothing to record there, which is itself useful information about how that particular tool behaves.

It is also worth timing the actual logging work rather than assuming it. Recording twenty prompts, tool by tool, with every column filled in as completely as the answer allowed, gives a real sense of how much time this kind of tracking takes in a normal week, rather than an owner guessing it will take either five minutes or an hour and being wrong in either direction.

The ten entry test is also where the column list itself gets pressure tested, not just the time it takes. Filling in real answers, rather than imagining hypothetical ones, tends to reveal which columns feel natural to complete in order and which ones an entry keeps skipping past. If a column is consistently skipped across real entries, that is a sign either that it is asking for something the tools rarely provide, in which case it stays but with the expectation that it will often be blank, or that it is redundant with another column already being filled in, in which case it is worth cutting rather than keeping out of habit.

One more thing worth recording during this test phase is which entries took noticeably longer than others, and why. An answer with a long list of cited sources takes longer to log accurately than a short answer with no sources at all, and noticing that pattern early helps set a realistic expectation for the ongoing monthly habit rather than being surprised by it later. It is also a reasonable point to decide how many prompts a normal month needs, rather than assuming every possible prompt variation must be checked every time. A small, fixed set of prompts checked consistently is more useful for spotting real change over time than a large, inconsistent set checked only occasionally.

Naming and storing screenshots

A spreadsheet row referencing a screenshot is only useful if that screenshot can be found later. A simple naming convention, the date, the tool, and a short prompt identifier strung together, keeps a folder of screenshots searchable months after they were taken, rather than turning into a pile of files named Screenshot 1, Screenshot 2, and so on that nobody can match back to a spreadsheet row.

The real test of a naming convention is not whether it looks tidy the day it is created. It is whether a file can still be found and correctly matched to its spreadsheet row a month later, once the context of why it was taken has faded from memory. Testing retrieval after time has passed, deliberately trying to find a specific screenshot using only the file name and the spreadsheet, is the only way to know whether a naming system works or just looks like it will.

A consistent folder structure matters as much as the file name itself. Keeping every screenshot in one place, rather than scattered across a phone's camera roll, a desktop, and a cloud drive, means the spreadsheet's screenshot column and the actual files stay reliably connected to each other, which is the entire point of recording a file name in the first place.

It is worth deciding on the file format for screenshots before the habit gets started rather than mixing formats over time. A phone screenshot, a browser screenshot, and a screenshot taken through a separate screenshot tool can all end up in slightly different formats and resolutions if no single method is picked and stuck with. That is a small detail, but a folder full of screenshots in consistent format and consistent naming is noticeably easier to scan through quickly than one where every file looks and behaves a little differently.

The same discipline applies to what goes into the screenshot itself. Including the full visible answer, not just the sentence that mentions the cafe, preserves the context around it, including any disclaimer text, related suggestions, or citation links that appeared alongside the answer. A cropped screenshot showing only the relevant line can save space, but it also throws away context that might matter later if the surrounding answer changes on a later check.

Reading it back once a month

A log that is only ever added to and never read back is not doing its job. A short monthly routine, sorting entries by date, counting how many times the cafe was named that month, counting how many times something in the answer was wrong, and listing any new source that showed up for the first time, turns the raw log into something an owner can act on.

That routine was tested against ten sample entries specifically to check whether someone who did not build the spreadsheet could still follow it. A tracking system that only makes sense to the person who designed it is a fragile one, especially for a small cafe where the person checking AI answers this month might not be the same person who set the log up originally. The goal was for that routine to be followable in well under ten minutes, sorting, counting, and listing, without needing any explanation beyond the column headers themselves.

What this kind of monthly read back cannot do is tell an owner whether logging itself is improving anything. Recording what AI tools say about a cafe over time does not, on its own, change what those tools say. What it does is replace a vague, month old memory of ah I think it got our hours wrong once with an actual dated entry, a screenshot, and a note, which is a meaningfully different and more useful thing to have on hand, whether that record ends up being used to correct a listing, to explain a pattern to someone else, or simply to know for certain what was said and when.

A spreadsheet like this also has a quiet second benefit beyond the monthly count. Over several months, the notes column tends to accumulate small, specific observations that are easy to forget individually but add up to something worth noticing together, a particular tool that consistently skips citing any source, a particular phrasing of a prompt that reliably surfaces the cafe when a shorter version does not, a competitor that keeps appearing in the same answers. None of those patterns are visible from any single entry. They only show up once enough dated rows exist to look back across, which is the entire reason this is worth doing as an ongoing habit rather than a one time check.

Frequently Asked Questions

What columns should a cafe owner's AI answer log include?

Date, time, which tool was used, the exact prompt typed, whether a location setting was applied, whether the cafe was named, whether the facts given were correct, which sources were cited if any, a screenshot file name, and a short notes field. That is nine columns, modeled on the structure Hillcane's own research log already uses, trimmed down for a single cafe.

Do I need more detailed columns, like a rating scale for how wrong an answer was?

No, fewer columns that get filled in every time beat a more elaborate structure that gets abandoned after two entries. The recommended log has nine columns, including date and time, tool, exact prompt, location setting, whether the cafe was named, whether the facts were correct, sources cited, a screenshot file name, and notes. A rating scale or sentiment score adds work without adding comparability, and the value of the log comes entirely from having entries you can line up month against month.

How should I name my screenshot files?

A naming convention combining the date, the tool used, and a short prompt identifier keeps a folder of screenshots searchable months later. The real test is whether a specific file can still be found and matched to its spreadsheet row after time has passed, once the original context has faded, not whether the naming looks tidy on the day it is created.

How long does logging AI answers take?

Testing this by recording twenty prompts across several tools, with every column filled in, gives an honest sense of the time involved for a given owner's own pace and tools, rather than assuming it. That timed test is a better guide than guessing, since the actual time can run either faster or slower than expected depending on how many tools are checked.

What if a column, like sources cited, is often left blank?

Note it and move on; a blank there is not a failure of the log. In the ten entry test of this layout, the sources cited column sat empty more often than any other, because not every AI answer names where its information came from. Recording that the tool gave nothing to cite is itself a useful observation about how that tool behaves. Keep the column rather than cutting it, since answers that do list sources also take noticeably longer to log accurately.

How often should I read back through the log?

Once a month is enough for most cafes. The routine has four steps: sort entries by date, count how many times the cafe was named that month, count how many times something in the answer was wrong, and list any source that showed up for the first time. It was built to take well under ten minutes. Over several months the notes column also reveals patterns no single row shows, such as one tool that never cites a source or a prompt phrasing that reliably surfaces the cafe.

Can someone else follow this log if I did not build it myself?

Yes, that was the point of the test. The monthly routine was run against ten sample entries specifically to see whether someone who did not design the spreadsheet could follow it using only the nine column headers and the sort and count steps, in well under ten minutes, with no extra explanation. That matters for a small cafe, where the person checking ChatGPT or Google's AI answers this month may not be the one who set the log up in the first place.

Does keeping this log improve what AI tools say about my cafe?

No, and it should not be framed that way. Logging what AI tools say does not, on its own, change what they say. It replaces a vague memory of an answer being wrong with an actual dated entry, a screenshot and a note, which is a more reliable record to act on or refer back to later.

Where should the screenshots be stored?

Keep every screenshot in one consistent folder rather than scattered across a phone's camera roll, a desktop, and a cloud drive. Name each file with the date, the tool, and a short prompt identifier, a convention tested by trying to retrieve specific files later using only the name and the spreadsheet. Pick one screenshot method and file format from the start, and save the full visible answer, including disclaimers and citation links, instead of cropping to the single line that mentions your cafe.

What is this log based on?

It is modeled on the log Hillcane uses in its own research work, a stored file with fields for city, query, tool, timestamp, the organic results returned, who showed up in the map results, whether a given cafe was named, and free text notes. That research structure was trimmed and adapted into a nine column version suited to one owner tracking one cafe. The layout was then tested against ten real entries, and the time to log twenty prompts was measured rather than guessed.

Work with Hillcane

A vague feeling that AI tools get your cafe wrong is not the same as knowing when, how, or how often. A simple spreadsheet is the difference.

If you want help setting up your own AI answer tracking log or reviewing what it turns up, reach Hillcane at hillcane.co/contact or call (256) 384-2449.