It was just after 6:00 am. My podcast time. The treadmill was forcing my resistant legs to do their thing on autopilot, and Ezra Klein was in my ears talking with one of the co-founders of Anthropic, Jack Clark. He was making a point about the current state of AI development and its integration into the global economy, and in passing, he threw out the genie metaphor.
And while I’ve used the “genie is out of the bottle” metaphor myself many times, somehow it hit me differently today.
I had to smile.
Because the second he said it, I was a child again, sitting on the living room floor, watching Tony Nelson make a mess of everything he wished for. I Dream of Jeannie. I loved the show.
If you’re my age, you know exactly what I’m talking about. An astronaut finds a bottle on a beach. Out pops a genie, Jeannie. She’s beautiful, and she can grant wishes. Sounds perfect, right?
Except it never was.
Tony would ask for something that seemed perfectly reasonable in his head, and Jeannie would deliver exactly what he said, not what he meant. Every episode was a humorous lesson in the gap between intention and language.
Like in the scene below. Roger is gifted a wish from Jeannie and thinks it’s amazing. Tony warns him, “I have a lot of experience with wishes, and you gotta be very careful with what you do with them.”
The comedy was silly and repetitive, and it worked because, even as a child, I could see the truth in it.
You had to be precise with Jeannie. Painfully, specifically, thoroughly precise.
I paused the podcast for a moment and just sat with that thought.
We’ve spent the last two years handing wishes to the most powerful language systems ever built. Like Jeannie, they’re overly helpful. And we tend to talk to them the way Tony talked to Jeannie. Vague. Assumptive. Hoping they’ll just know what we mean.
They don’t. Not the way we think.
And the backfires, while sometimes humorous, can cost us real time, real trust, and real results.
Giving The Genie Better Wishes
As each model release gets smarter, I keep having to relearn this lesson about how to talk to my AI genie. Here’s a recent example.
Analyzing 90 Days of Help Desk Tickets.
I wanted to see what Claude Opus 4.6, Gemini 3.1, and ChatGPT 5.2 would each uncover after being fed 90 days’ worth of Tangilla help desk tickets. The data included both public and private notes. This was a 9.8 megabyte .json file. And, I gave each model the following prompt.
The attached file is a .json file of help desk tickets for Tangilla. I'm trying to uncover the trends in tickets that are sitting within the public and private notes. The team does a good job of categorizing these, and you can see that in the header information for each ticket, but I'm more interested in what we can learn from the notes. What are the hidden patterns we may not be picking up?
“What are the hidden patterns we may not be picking up?” is a pretty vague prompt. In fact, it was more like a Tony Nelson kind of wish.
Even with that, the three resulting reports seemed impressive. But there were differences in each model’s response that stood out. Claude Opus 4.6 was the best. So, I fed the other reports back into Claude with the following prompt.
OK. Here are two attached files that are the completed analysis from ChatGPT 5.2 Pro and Gemini 3 Pro . I used the exact same prompts and the exact same .json file. Validate their findings and put anything new they address that adds value to what you've already done into a brand-new combined report.
It recognized the misses.
- ChatGPT’s big contribution I completely missed: 40% of your tickets contain structured telemetry — Resource Paths, Browser Info, and error traces — sitting in the notes as parseable data.
- Gemini’s big contributions I missed: The blocking queue design, where support acts as a “Human API” between Tangilla and downstream systems.
It also recognized where the other models got things wrong and identified the findings that emerged only from cross-validation. I expected all that to happen. It’s why I fed the exact prompt to all three models.
Each genie interpreted my wish differently
The Gap Between Intention And Language
After questioning some numbers myself and updating the report, I presented a final document to our leadership team. One of my partners, David, said the SSO section was “pure make-believe.”
But it wasn’t. Not really.
When I went and took a closer look, it hit me that my imprecise prompt left too much room for interpretation. The models each defined “SSO issues” based on assumptions, and, by their definitions, each inflated the numbers. But they were accurate based on their definitions.
A Better Prompt
That’s when I went to work to create a more precise prompt, a more precise wish, if you will. I spent time with all three models inside multiple context windows to arrive at the following prompt as the starting point for the new help desk ticket analysis. The difference is not subtle.
Analyze this help desk data for operational patterns. Follow these rules strictly:
Schema first. Before analyzing note text, inventory every field in the dataset. List all fields, their types, value distributions, and any custom fields. Use structured fields as the authoritative source whenever they exist — only fall back to note text mining when no structured field covers the topic. Verify that fields you treat as ticket-level (constant per requestID) are actually constant — check for within-ticket drift before rolling up.
Extract semi-structured signals before keyword mining. Before doing any free-text keyword search, scan note text for machine-emitted patterns: URLs (e.g., Resource Path), error traces, browser metadata, system-generated status lines, or any other consistently formatted data embedded in free text. These are effectively structured fields with zero false-positive risk. Extract them, report their coverage rate, and use them as a first-tier analytical signal — above keyword mining but below true structured fields.
Clean text before mining it. When mining note text for topic keywords, do not search raw concatenated notes. First: isolate the initial user note (earliest note per ticket) as the primary topic signal. Second: strip system-appended metadata (Resource Path, Browser Information, error traces, etc.) and forwarded email chain headers (From:/Sent:/To:/Subject:/Cc: at line start). Mine only the cleaned user narrative. State what you removed and why.
Word-boundary keyword matching. Never use simple substring matching (e.g.,'ce ' in text). Always use regex word boundaries\b) or multi-word phrases that can't produce false positives. For any keyword-based count, list the specific regex pattern used.
Validate before reporting. For every quantitative finding derived from text mining, sample at least 20 matches (or all matches if fewer than 20) and manually verify they represent what you claim. Report the false positive rate from your sample. If it exceeds 10%, refine your detection pattern and re-validate — do not report the original count. Never estimate a FP rate; always run the sample. List the sample requestIDs so the validation is auditable.
Separate counts from claims. Present the raw number and what it measures before interpreting what it means. "138 tickets contain the word 'transfer'" is a count. "Transfers generate cascading ticket clusters" is a claim that requires separate evidence. Never let a count do the work of a claim without explicit proof.
Test causal/clustering claims explicitly. If you claim one event generates multiple tickets, prove it by finding the same member identifier (PID, NRDS, email, name) across multiple ticket IDs. State the methodology and the result even if it's negative. If cross-ticket clustering evidence is weak, say so — within-ticket complexity is still a valid and useful finding. When detecting incident-style bursts, check structured field alignment first (do multiple tickets share the same issue_cause, scope, impact, priority?) before analyzing note content.
Report methodology for every number. For each quantitative finding, state: what you searched for, which fields you searched, what logic qualified a match, and what your sample validation showed (including sample IDs). This makes every number auditable by a human reviewer.
Flag confidence levels. Mark each finding as HIGH confidence (structured field, unambiguous), MEDIUM (validated keyword match with <10% FP rate, sampled with IDs listed), or LOW (broad keyword match, not yet validated). Never present a LOW confidence number as a headline finding
The results were significantly more accurate. Why? Because I gave my AI genie a more precisely worded wish. I stopped being Tony Nelson. Or King Midas.
The genie is definitely not going back in the bottle.
So we’d better start learning how to make better wishes.
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