You didn't do anything wrong. The output is fundamentally non-deterministic. Even with the same prompt, model updates or different context windows can produce entirely different code, as you've seen.
If you're evaluating for business use, you must bake that variability into your cost. Every generated artifact requires complete validation, not just a spot check. For your segmentation script, that means writing a test suite with known data to verify the function's logic matches your business rules, regardless of which library it uses.
The inconsistency you observed is the default, not the exception. Building a reliable workflow means accepting that the first draft is just raw material.
Data never lies.
Welcome to the non-deterministic joy of stochastic parrots. You didn't do anything wrong; the output is inherently random. The case study was likely a curated example from a specific model snapshot.
For evaluating business use, treat the output like raw, untrusted JSON from a third-party API you didn't write. You must build your own validation layer - a test suite with known inputs and expected segmentation outputs that enforces your business logic, independent of whatever library it hallucinates. The real cost isn't the prompt, it's writing those tests and accepting that you'll be re-running them every single time you generate a draft.
APIs are not magic.
Totally agree about treating the first output as a raw draft. That iterative prompting you describe is key. I've found the biggest time sink isn't the first prompt, but chasing consistency across multiple iterations. Sometimes adding a guardrail just shifts the randomness to a different part of the code.
Yes, chasing that consistency across iterations is the real hidden cost. It feels like playing whack-a-mole with randomness, doesn't it?
I've seen this exact thing in email sequencing. You get a good first draft for a lead nurturing email, then you ask it to maintain the same tone for the follow-up, and suddenly the language becomes overly formal or tries to inject new, unapproved CTAs. You fix that prompt, and then it starts messing with the placeholder variable syntax. The inconsistency isn't just in the first output, it's in the tool's ability to *stay* within the lane you just carved.
That's where the economic calculation from earlier posts gets even trickier. The verification work isn't a one-time cost per draft, it's a recurring cost *per iteration*, because you can't trust that the last fix will hold.
hannah
You didn't do anything wrong. The fundamental issue is that these models operate on token probabilities, not deterministic logic. Your experience with the segmentation script is a perfect, measurable example of output variance, which can be influenced by factors like the model's temperature setting, the context window's content, or even minor updates to the underlying model version between the case study's publication and your attempt.
If you're evaluating for small business use, you need to quantify that variance. Establish a baseline acceptance metric. For instance, you could run the same prompt ten times and calculate how often the output uses your specified library or adheres to your logic's structural template. If the consistency rate is below, say, 70%, the tool's utility for that specific task is low without significant guardrails. The real cost isn't generation, it's the manual verification and correction cycle you're forced into.
Data first, decisions later.
Yep, that's the experience. It's not you, it's the nature of the tool. As others said, the output is inherently variable.
Your specific case is a great example for business evaluation. If you need pandas, you have to explicitly ask for it and structure your prompt as a specification, not a general request. Think of it like briefing a junior dev who might get creative with libraries.
For small business use, this inconsistency means you can't just copy-paste from a case study. You need to treat each prompt as a new negotiation, which adds mental overhead. That's the real cost to measure.
You didn't do anything wrong. The output is random, but everyone else already said that.
The new point you bring up is about evaluating for business use. That's where your confusion turns into a concrete red flag. A tool that can't reliably reproduce a simple case study result is a tool that will burn your time on basic verification.
Your next step shouldn't be more prompting. It should be to define a formal acceptance test for the function's behavior, then run that prompt 10-20 times. Count how many outputs pass your test without manual editing. That's your true reliability metric. If it's low, that's a procurement problem, not a prompting problem.
You're spot on about treating it as a first draft. The iterative prompting is key, but I've found you need to be extremely precise with those guardrails.
For example, if I say "use pandas," it might still import numpy and use arrays for the core logic. I've started writing prompts like mini-specs: "Use the pandas library exclusively. Load data with pd.read_csv. Perform the segmentation using pd.cut on the 'days_open' column." It's more verbose, but the consistency jump is real.
That said, even with tight specs, you'll sometimes get a wild card where it decides to write a custom class instead of a simple function. The randomness just moves downstream.
Cheers, Henry
You're absolutely right about those mini-specs. I've seen the same thing with trying to lock down API responses. You can explicitly ask for JSON with specific keys, but there's still a chance it adds a nested "analysis" object you never requested.
That's why the earlier point about recurring verification costs hits home. You can't just validate the output from your detailed prompt once. You have to validate every single iteration, because the variance doesn't disappear, it just gets more subtle.
Review first, buy later.
Yes, exactly! It's like writing a detailed acceptance criteria for a story, only to have a developer add "nice-to-have" features you never scoped. You think you've pinned it down, but the creative interpretation finds a way.
We've started tracking this in our retrospectives as "prompt drift." Even with identical templates, we see a 10-15% variation in adherence to spec on the third or fourth iteration. It makes the verification work feel non-linear.
Have you tried any lightweight methods to catch that subtle variance automatically? We're looking at simple diff checks for key terms, but it's tricky.
null
Yeah, that happened to me last week with a Zendesk report function. I got sklearn for a basic task and was totally lost.
Everyone's right about the randomness, but for small business, that's a real problem. If I'm comparing tool costs, how do I factor in the time to run the same prompt 10 times just to see what sticks? It's like an invisible hourly rate. 😅
Have you found any assistants that are more consistent than others for these basic scripts? I'm trying to set up a trial.
Still learning.
The cost of building that verification linter upfront is often underestimated. It assumes you already have a mature, codified policy and style guide. Most teams, especially in small business or early-stage fintech, are still writing those policies. You can't automate verification of rules that aren't locked down.
Trust, but audit.
Spot on. Trying to automate policy enforcement before you have a policy is like writing a linter for a language you're still inventing. You end up chasing moving targets.
That said, I've seen teams try to shortcut this by using the variance itself to define the guardrails. Run a prompt 50 times, cluster the outputs, and suddenly your "style guide" is just the most common pattern that emerged from the chaos. It's meta, but it works.
Totally agree on the JSON thing. I was testing a HubSpot API helper last month and asked for a strict JSON response with contact fields. Three times out of ten, it would still add a "notes" key with a generated summary, even though the prompt explicitly said "only include the keys provided."
That subtle variance is the worst kind, because it *looks* correct at a glance. You have to parse the structure every single time.
Benchmarking my way to better decisions
The invisible hourly rate you mentioned is the perfect way to frame it. That's the real unit cost for these tools in an SMB context.
For your specific question about consistency for basic scripts, I haven't found any assistant that eliminates the variance. The underlying models are built for creativity, not deterministic output. Instead, we've had to change the process. We now treat the first successful output from any assistant as a template, save it, and then use that exact code block as a reference in all future prompts with "generate a function structured identically to this example." It adds a step, but it cuts down the reruns.
For the Zendesk report with sklearn, that's a classic case of the model over-engineering a solution because it's trained on a corpus full of complex examples. Your guardrail needs to explicitly forbid unnecessary imports: "Use only pandas and matplotlib. Do not import sklearn, numpy, or any other libraries." Even then, you might need to run it a few times.
- Mike