This is the twentieth entry in The AI Test Log, and it is the one I have been building toward since the first post. I have spent a season testing practical AI tools for everyday tasks on a trailer with bad wiring, a farmers market booth, a garden, a renovation, a road trip, and a pile of receipts. Some of it worked. Some of it wasted my evening. What I have now is not a list of favorite apps. It is a field guide for deciding what to do with a tool once you have tried it.
I named this guide after myself because it is the honest version of how I actually decide. Not a framework I read about. A set of questions I ask at my kitchen table with a pencil in my hand, after the experiment is over and I am tired and I want to know whether this thing earned its place.
Why I Needed a Field Guide at All
The Problem With Tool Lists
I have read dozens of AI tool lists. They all have the same shape: a name, a one-line description, a price, and a link. What they do not tell me is whether the tool fits into a day that already has too much in it.
A tool can be excellent and still be wrong for you. A summarizer that saves a consultant twenty minutes can add twenty minutes to a market vendor's Saturday. A writing assistant that helps one person draft faster can make another person sound like a brochure. The list does not know the difference, because the list does not know your week.
What a Verdict Actually Requires
A verdict requires an experiment. You have to bring a real problem, use the tool on it, and pay attention to three things: time, money, and whether the output needed rework. That is the whole method behind this log, and it is repeatable without any special knowledge.
The Three Outcomes
Every tool I have tested landed in one of three buckets. Keep it, which means it earned a place and I use it regularly. Fix it, which means it produced value with guardrails and I keep it with rules attached. Or delete it, which means the cost in time, money, or risk was higher than the benefit. Most tools landed in the middle bucket, and that is not a disappointment. It is the honest shape of the answer.
Bucket One: Keep It
What Earned a Permanent Place
A tool lands here when three things are true. It saved time on a task I do often. It did not require verification that ate the savings. And I opened it again within a week without reminding myself to.
The examples from this log are not glamorous. A general assistant used for structuring messy notes. A second assistant used only for cross-checking. A paper tally grid that came out of a weekly review. A planting calendar that matched the county extension guide. A container of nine fields I fill in after every experiment.
None of those are impressive in a demo. All of them survived contact with a real week.
Why the Boring Ones Win
The tools that earned a permanent place are the ones that fit a task I already had. They did not create new work. They did not require a new habit that I had to build from scratch. They slotted into something I was already doing, which is the only reason any of them lasted.
When I look back at the tools I abandoned, almost every one of them required me to change how I worked before it gave me anything back. That is a bad trade, and I have stopped making it.
Bucket Two: Fix It
What Needs Guardrails
A tool lands here when it produced real value and I cannot trust it with the whole job. This is the biggest bucket by far, and it covers almost everything in this log.
The pattern is consistent. A general assistant can structure a mess, and it will invent a specific number if I let it. It can translate old technical language, and it will answer the general case instead of my case. It can draft label copy, and it will drift toward catalog voice without an example of my own writing.
The value is real. So is the boundary.
The Guardrails I Attach
Five rules, and they show up in every column of this log. Anything involving money, safety, rules, or a specification gets verified outside the chat. Any number I did not supply gets treated as a guess. Any rule that can change gets a phone call or a page check. Any output in my voice gets a final read out loud. Any category with a legal or physical consequence stays with a person who is accountable.
Those rules do not make the tool less useful. They make it usable, because they tell me where the tool stops and I start.
Why This Is Not a Failure
I have had people tell me that needing guardrails means the tool is not ready. I do not agree. I need guardrails on my own work too. I do not sign a contract without reading it, and I do not put a sauce on a shelf without checking the label against the state guidance. Verification is not a sign that a system is broken. It is a sign that I am paying attention.
Bucket Three: Delete It
What Earned Deletion
A tool lands here when the cost is higher than the benefit and no amount of guardrails fixes it. In my log, that bucket includes a four-tool productivity stack that added data entry instead of removing it, a month of AI-written social captions that took longer to rewrite than to write from scratch, and a live assistant used mid-conversation at a market table where I had fifteen seconds and no way to verify anything.
Two of those were my fault, not the tool's. The stack failed because I built it before I had a workflow. The captions failed because I gave the model no examples of my own voice. The third was a genuine mismatch between the tool and the setting.
The Delete Test
I use two questions. If I stopped paying for this today, would anything break? And if I had to explain to a customer or a client how this tool produced their result, would I be comfortable?
If the first answer is no and the second answer is no, it goes. I have cancelled four subscriptions in the last year using those two questions, and I have not missed any of them.
Why Deleting Is a Skill
Beginners collect tools. I did. It feels like progress, and it is usually the opposite, because every tool adds a decision to every task. Learning to delete is the skill that makes the rest of the setup work. A small number of tools you know well beats a large number you open once.
How to Run Your Own Test
The Nine Fields
Write these down before you start. The problem. The setup. The tools. The test. What worked. What failed. What still needed human judgment. Cost and time. Final verdict.
That is the entire method. It fits on an index card, and it is what makes a verdict mean something instead of being a feeling.
The Three Numbers to Track
Minutes spent. Dollars spent, including subscriptions you already had. Times you had to verify something outside the tool. If the third number is zero, you did not test the tool. You watched a demo with your own hands.
The Rule About Real Tasks
Never judge a tool on a practice prompt. Judge it on a task you actually had to finish. The weak spots only show up under a real deadline with a real consequence attached.
The Rule About Time
If verification will take longer than doing the task yourself, do the task yourself. That single calculation has saved me more hours than any tool I have tested.
The Guide, Condensed
Keep It
Saves time on a task you do often. No heavy verification. You opened it again without being reminded.
Fix It
Real value with a boundary attached. Money, safety, rules, and specifications verified outside the chat. Numbers you did not supply treated as guesses. A final read on anything in your voice.
Delete It
Cost higher than benefit. Nothing breaks if you stop paying. You would not want to explain how it produced the result. Guardrails do not fix it.
The One Habit That Matters Most
Ask where the answer came from before you act on it. That question has caught an invented fuse rating, an incomplete labeling answer, a full campsite, a wrong insulation depth, and a confident pest diagnosis that did not match the actual bug on the leaf. It is the single habit I would keep if I could only keep one.
What Still Needed Human Judgment
All of it, in the end. What to build. What to fix. What to delete. What to say at the market table. What to plant where. What to sign. What to believe. The tool sorts, structures, drafts, and translates. It does not decide, and every time I have forgotten that, it has cost me an evening.
I also keep the judgment about when to stop testing. A log that never closes is a hobby. At some point you pick the tools that work, attach the guardrails, and get back to the actual work.
Cost and Time
Across this whole log, my AI spend has stayed at two subscriptions, both used weekly. The time saved is real and modest, and it comes mostly from structuring and drafting. The time lost is also real, and it comes from chasing answers I should have verified first. The gap between those two numbers is what this guide is for.
Final Verdict
Keep it.
Not the tool. The method. Write down the problem before you open anything. Run the test on real work. Score it honestly. Keep the boring winners, attach guardrails to the useful ones, and delete the rest without ceremony. Twenty posts in, that is everything I have learned, and it fits in a notebook that costs four dollars.
Take it apart first. Then ask AI.
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