How I got caught trusting AI and had to apologize in a meeting

I got caught trusting AI today.

Then I had to explain myself in a meeting.

Then I had to apologize.

The AI gave me a wrong answer about a basic part of my own job. I have explained this kind of thing to other people plenty of times. I should have known it cold.

The answer was wrong in a stupid, boring way. Nothing dramatic. No fake historical figure. No imaginary research paper. No wildly unhinged claim about SEO being powered by lunar tides.

It was a small mistake that sounded completely reasonable.

I read it, understood the words, and kept moving.

That was my mistake.

I had become a prompt and produce user.

I know better than this

I use AI every day.

I use it for brainstorming, outlines, research starting points, rewrites, technical explanations, content angles, and the occasional attempt to untangle a problem that has been sitting in my brain like a tangled headphone cord.

AI makes a lot of my work faster.

It also gives me answers in a calm, confident voice even when the answer is wrong. OpenAI says ChatGPT can produce incorrect information and recommends checking important facts against reliable sources.

I know this.

I write about AI search. I help businesses think about how their content gets interpreted by answer engines. I tell people to review AI output before it reaches a customer, a client, a website, or a public post.

Then I skipped the review step myself.

That is the embarrassing part.

I did not fail because I lacked information. I failed because I let a fluent answer bypass my own brain.

The prompt stage

The first P is Prompt.

Prompting is the part everybody talks about. Write a better prompt. Add more context. Give the model a role. Specify the format. Include examples. Ask it to show its reasoning or list its assumptions.

Good prompts help.

A vague prompt gives the system plenty of room to guess what I mean. A detailed prompt gives it a better target.

When I am working on SEO content, I might include the audience, the search intent, the page goal, the relevant product, the compliance limits, the tone, and the facts that must remain unchanged.

That reduces avoidable nonsense.

It does not turn the AI into an expert witness.

A prompt gives the system instructions. It does not give the system perfect judgment. It does not make the model automatically understand the latest Google documentation, a client’s internal process, or the exact detail I have learned through years of doing the work.

A better prompt improves the starting conditions.

It does not remove my responsibility.

The produce stage

The second P is Produce.

This is where the AI generates something.

It produces an answer, outline, list, explanation, code sample, summary, image prompt, or draft. Sometimes the result is excellent. Sometimes it gives me a useful pile of raw material. Sometimes it writes 900 words to avoid answering a question that needed 12.

The output can look finished long before it is ready.

That visual polish creates a dangerous little shortcut in my head. The paragraphs are clean. The sentences connect. The answer uses the right vocabulary. My brain sees familiar words and starts treating the whole thing as familiar knowledge.

That is how a wrong answer slips through.

AI can get a date slightly wrong. It can combine two similar concepts. It can apply a rule from one platform to another. It can confidently explain a feature that changed six months ago. It can invent a source that sounds real enough to waste an afternoon.

In SEO, tiny inaccuracies stack fast.

A wrong detail about indexing can lead to bad technical recommendations. A muddled explanation of canonical tags can create a messy implementation. A mistaken assumption about structured data can send a developer toward the wrong fix. A small error in search intent can shape an entire content plan around the wrong audience.

None of those errors need to look ridiculous.

They only need to sound plausible.

The ponder stage

The third P is Ponder.

This is the part I skipped today.

Ponder means I stop and engage with the output before I use it.

I ask myself a few basic questions.

• What is the actual claim here?
• Do I know this already?
• Does the answer match what I have seen in real work?
• What source supports it?
• Is the information current?
• What would happen if this detail were wrong?
• Am I agreeing because it is correct or because it is written smoothly?

Ponder is the human brain coming back online.

It is the basement nerd stage of the process. The part where I pull out the mental filing cabinet, turn on the fluorescent light, and start checking labels.

What happened in the meeting

I used AI to help me answer something quickly.

The output gave me a confident explanation.

I accepted it because it sounded familiar. I did not verify it. I did not compare it against my own knowledge. I did not open the relevant documentation. I did not even pause long enough to think, “Wait, is that actually how this works?”

Then the issue came up in the meeting.

Someone asked a reasonable follow-up question. The answer started falling apart.

I realized I had repeated an AI-generated mistake as if it were my own understanding.

There is a specific flavor of humiliation that comes from being corrected on something you should know.

I had to explain that I had trusted an AI answer without checking it. Then I apologized.

Saying that out loud was rough.

I could have blamed the tool. That would have been easy. It also would have been dishonest. The tool produced the wrong answer, and I chose to pass it along.

That part belongs to me.

Small errors compound

People often talk about AI errors as dramatic hallucinations.

The more common problem is probably quieter.

It is the wrong adjective in a client draft. The outdated platform detail in an internal explanation. The statistic that looks close enough. The source that was never checked. The recommendation built on one bad assumption.

Small errors are easy to overlook because correcting them feels unnecessary.

One tiny mistake can survive a quick review. Then another person copies it into a document. Someone else uses the document to make a decision. A page gets published. A client gets advice based on a detail nobody stopped to inspect.

The error gathers authority as it travels.

That is why the third P matters.

Ponder creates friction before the answer becomes part of the workflow.

NIST’s AI Risk Management Framework makes human oversight, reliability, transparency, and risk management part of responsible AI use. My three P framework is the very small, very practical version I can use while sitting at my desk with coffee nearby.

My new three P workflow

I am keeping the process simple.

Prompt

Write the request clearly. Include the context, audience, goal, constraints, and known facts. Tell the tool what kind of output I need.

Produce

Let the AI generate the draft. Use it for speed and pattern recognition. Treat the result as working material.

Ponder

Stop before sending, publishing, presenting, or relying on the answer. Check the important claims. Compare them with trusted sources and my own experience. Rewrite anything that feels vague, overconfident, or suspiciously convenient.

For low-stakes brainstorming, the pondering step may take a few seconds.

For client recommendations, regulated industries, technical changes, medical topics, legal topics, financial information, and anything that could cause real harm, the review needs more time.

I also need to pay special attention to answers that confirm what I already wanted to believe. Familiarity can create a false sense of accuracy. So can a polished tone.

The smoother the answer feels, the more deliberately I need to inspect it.

I am still using AI

This experience did not make me quit AI.

I am still going to use it. It helps me work through ideas faster. It catches patterns I might miss. It gives me starting points when the blank page is being especially rude.

I am putting my brain back in the workflow.

AI can prompt me. AI can produce material. AI can help me explore a problem from several angles.

I still have to ponder.

That pause is where expertise shows up. It is where I notice that a familiar answer has one weird piece glued onto it. It is where I check the source, question the assumption, and decide what deserves to ship.

I tell clients that AI-assisted work needs human review.

Today, I got a very direct reminder to follow my own advice.

Prompt. Produce. Ponder.

The third P is the one that keeps me from apologizing in the next meeting.

For more writing about search, AI answers, and the work behind the algorithm, visit the Randi Bagley blog. You can also read about how I approach AI visibility for dispensaries and why I am adding GEO data to SEO reporting.

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