You’ve been there. You spend hours crafting the perfect prompt—clear, specific, full of context. You hit enter. The first response is brilliant. You reload. Same prompt. The second response is gibberish. Your stomach drops. This isn’t your fault. It’s the dirty secret nobody in AI wants to admit: LLMs are non-deterministic black boxes, and we’re building critical infrastructure on top of them.
Here’s what actually happens when you integrate AI into a real product. You don’t just trust one model. You compare. You cross-reference. You keep a spreadsheet of outputs from GPT-4, Claude, Gemini, maybe even a local Mistral. You’re not looking for the best—you’re looking for the least wrong. And that’s the unspoken workflow that every developer ends up with.
A developer named vseryakov posted a simple example on GitHub recently. He said: ‘I realized I am comparing results all the time. Seeing output from many LLMs sometimes has surprises.’ The comment section erupted not because the code was clever, but because it was honest. The real meta-skill in AI engineering right now isn’t prompt design—it’s ad-hoc human-in-the-loop A/B testing.
Think about what that means. We are building software that’s supposed to be deterministic—banking, scheduling, legal, medical advice—using engines that can change their answer on the same input. And we’ve been sold a story that the magic just works. It doesn’t. If you’re constantly comparing outputs from different models, you’re not doing it wrong. You’re doing the only sane thing.
The industry loves to talk about ‘agentic’ systems and ‘autonomous’ workflows. But the reality is that every time you hit send on a prompt, you’re rolling dice. The variance is so high that the only way to get reliable behavior is to run the same thing three times and vote. Or route different question types to different models based on their known strengths—which you only discover by, again, comparing.
I saw this firsthand at a startup last year. They had a Slack bot that summarized channels. At first, it worked great. Then the model updated—no announcement—and suddenly every summary started with ‘I’m an AI, I cannot…’ They had to roll back. They had to build a comparison dashboard. They became a model-agnostic routing company by accident.
This is not a bug to be fixed. The practical utility of LLMs is bottlenecked by output variance, and until we either fix the determinism or accept the chaos, the developer’s job is to be the glue. The golden quote for your next team meeting: ‘We don’t ship AI products. We ship human-mediated probabilistic approximations that we pretend are deterministic.’
So stop looking for the perfect prompt. Stop feeling like you’re failing because you can’t get consistent results. Take a side: this is brilliant because it forces us to treat AI as a tool, not a magic box. And it’s dangerous because we’re lulled into trusting it when we shouldn’t. Your real value as an engineer lies in the comparison, the routing, the human judgment that decides which model to trust today. That’s not failure. That’s the job.
FAQ
Q: Is it really that bad? Can't you just use one model and tune it?
A: No. Even with fine-tuning, output variance persists because of temperature, sampling, and the inherent non-determinism of the architecture. You can reduce it, but you can't eliminate it without sacrificing the model's creativity.
Q: What's the practical implication for a team building on top of LLMs?
A: You need a routing layer and a comparison workflow built into your product, not just a single API call. Budget for human oversight. Treat every model response as a candidate, not a final answer. And never trust a 'v1' without a fallback.
Q: Isn't this just a temporary problem? Won't future models be deterministic?
A: Maybe, but the for-profit race is pushing toward bigger, more stochastic models—not deterministic ones. OpenAI, Anthropic, and Google are investing in capability, not consistency. So expect this problem to get worse before it gets better. The contrarian bet is that the winners will be those who build reliable comparison tools, not better prompts.