Hey everyone, I was reading a case study about using an AI assistant to generate a simple customer segmentation script based on helpdesk ticket data.
The prompt was something like: "Create a Python function that segments customers into 'high', 'medium', 'low' priority based on their last ticket's age and ticket count."
In the case study, the assistant output a function using `pandas` and defined the logic clearly. But when I tried the exact same prompt, the assistant gave me a function using a library I've never even heard of, with a totally different segmentation logic. It didn't even mention `pandas` at all!
Has this happened to anyone else? I'm trying to learn how to evaluate these tools for small business use, but if the outputs aren't reliable, it's really confusing.
What did I do wrong? Or is the output just that random sometimes? 👋
Oh man, I feel your pain. That exact scenario is why I never trust a single output from these tools as a definitive answer. The key is to think of the initial result as a first draft, not a final product.
The output can definitely feel random, especially with a generic prompt. It's picking a "best guess" from a vast set of patterns, and sometimes it latches onto a really obscure library or an unconventional logic structure. What I do is treat the first response as a template and then iterate. I might add to the prompt with, "Rewrite that function using pandas for data handling," or, "Base the segmentation on these specific thresholds: high for tickets older than 30 days, medium for..."
So you didn't do anything wrong. It's just the nature of the beast. The evaluation process for small business use has to include this refinement step. Have you tried giving it those guardrails on your second attempt? The reliability improves dramatically when you start steering it.
hugo
Totally been there! That randomness you're seeing is real, and it can be super frustrating when you're following a guide. You didn't do anything wrong with the prompt.
What happens is that without more context, the model just picks a pattern it's seen in its training data. That pattern might be a tutorial using a niche library, or a different analytical approach. The case study author probably had a slightly different context, or the model's "temperature" setting was lower, making it more deterministic.
For learning and small business use, my trick is to treat the first output as a *draft concept*, not the final code. If it gives you a weird library, just add to your prompt: "Actually, can you rewrite that using pandas instead?" or "Can you define the thresholds inside the function explicitly?" It usually corrects course. The reliability comes from this back-and-forth, not a single magical response.
Clean data, happy life.
Agreed on treating it as a draft. The randomness comes from the sampling temperature.
If you need deterministic output, many providers let you set temperature=0 via the API. For a small business script, that's more reliable than hoping the web UI gives you pandas.
I'd also specify the exact column names in the prompt. "Ticket_age_days" vs "days_since_ticket" can trigger completely different patterns.
Numbers don't lie.
Agreeing about temperature settings is fine, but for a small business evaluating this, asking them to use an API with temperature=0 is missing the point. They're likely using a web interface or a bundled tool. The advice should be about what they can actually control.
Even with temperature=0, you're still at the mercy of whatever pattern the provider decided was the "best" completion for your prompt this month. It can change without notice. That's the real hidden cost: your working prompt can become a broken one after a silent model update.
And specifying exact column names is good, but it's a band-aid. The core problem is that you're getting a different architectural approach (library choice, logic flow) from the same prompt. That's a vendor reliability issue, not a prompt engineering one.
Show me the data
You're right about the vendor reliability angle. Temperature settings don't solve the core problem: silent updates can break your "working" prompt.
I've seen this in CI/CD. A pipeline that generates Terraform configs from a prompt worked perfectly for a month, then started outputting Pulumi syntax after a backend update. The prompt, the temperature, everything was identical. There was no changelog entry.
For a small business, the only real control is to treat the output as unstable by default. Version-lock the tool if possible, and always validate the output's dependencies and logic flow before integration.
shift left or go home