1 prompt, 74 tries, 0 Matches: why “prompt magic” isn’t real
In my free time, I like browsing the ChatGPT subreddit and seeing how the world is using and reacting to AI. One recent viral Reddit Post tried something deceptively simple: it entered an image prompt into ChatGPT-Omni, and prompted it to “create an exact replica of the image, and don’t change a thing.” They did this 74 times.
The difference in result is striking. See below the original photo provided, and the 7th, 14th, and 74th replicated image, each more different than the last.
There’s a seductive myth I hear a lot around AI: if you can craft the perfect prompt, you’ll unlock everything you ever wanted. This is false, and this viral example highlights a really important shortcoming in AI.
Why Generative AI Fails at Repetition: It’s Built on Probability, Not Precision
At the core of every generative AI model, whether it’s producing text, images, or code, is a probability engine. These models are not deterministic systems with hard-coded outputs. Instead, they are trained to predict the most likely next word, pixel, or token based on what they’ve learned from huge generalized datasets.
Imagine asking a model to complete the sentence “the sky is…”
The model doesn’t “know” the sky is blue. It looks at all the times it’s seen that phrase during training and picks the most statistically likely continuation. Maybe it chooses “blue.” Maybe “clear,” “overcast,” or “beautiful.” It depends on:
- The context of the full prompt.
- The temperature setting (which controls randomness).
- And the inherent stochasticity of the generation process.
Even if you give it the exact same input twice, the model may generate different outputs because it's not retrieving a fixed answer. It's sampling from a distribution of possibilities.
Why This Matters Beyond Reddit
The underlying strength of generative AI - its ability to creatively interpolate between examples - is also its greatest weakness in structured environments and business applications.
Here’s the thing: creative chaos doesn’t scale to operational use. In the real world, especially one as technical as automotive, consistency matters. And this is where the “prompt magic” narrative begins to fall apart. In business processes, you need reliable systems that are repeatable by design.
Building AI for the Real World: Bringing Structure into the Chaos
If generative AI is inherently probabilistic, does that mean it’s doomed to be unreliable in precision use cases?
Not at all. But you have to build for precision on purpose. That means moving beyond general prompts and creative outputs and designing systems that prioritize structure, repeatability, and domain expertise from the start.
Here are some systems we have at Versable to bring structure into the chaos:
1. Don’t just prompt, scaffold
Most people treat generative AI like a magic 8-ball: ask a vague question, hope for a good answer.
Instead, you need to guide the model with structured workflows:
- Break complex tasks into clear steps (e.g., “classify part category → map to PCdb code → generate listing”). Use system prompts and function calls to enforce format. Build reusable templates that define inputs, outputs, and logic.
This transforms the model from an improviser into a collaborative tool within a process.
2. Finetune for domain expertise
Generalized models are trained on the entire internet. They’re good at sounding smart across many topics—but bad at being accurate in any one of them.
Precision comes from domain adaptation:
- Fine-tune the model on industry-specific language, data, and structures.
- Train it to recognize part types, product hierarchies, and catalog standards
- Use your own high-quality data as a foundation.
3. Add non AI guardrails for post processing
Even a good model can go off-script. That’s why it’s critical to:
- Validate outputs with logic-based rules (e.g., “UOM must be consistent with part category”).
- Use dictionaries, label sets, and product taxonomies to constrain results.
- Implement deterministic post-processing to ensure consistency across runs.
AI gives you creativity. Guardrails give you confidence.
4. Evaluate for Repeatability, Not Just Wow Factor
Don’t just test if the model works once—test if it works every time.
- Run batch tests with identical prompts and compare outputs.
- Flag variations that affect accuracy, not just wording.
- Optimize for replicability in structured tasks, not novelty.
At Versable, this is exactly how we approach AI: not as magic, but as engineering.
We don’t chase “perfect prompts.” We build systems that are intentionally structured, precisely scoped, and obsessively repeatable, because that’s what our customers demand and deserve.
PS: welcome to our second blog post! I'm Christina - founder and CEO of Versable. We're always looking for guest collaborators on future blog posts. If you want to discuss data, AI, or the aftermarket and are interested, shoot me an email at tina@versable.ai
