Building with LLMs: What Nobody Tells You About Edge Cases

The edge case that cost us a weekend

We were two weeks post-launch at Sonic Linker when a client reported that our AI was randomly splitting their documents in weird places. Not all the time. Just... sometimes.

Turns out, the LLM was treating certain punctuation marks inconsistently. A comma in one context was fine. The same comma in a slightly different sentence structure? The model thought it was a period. We spent an entire weekend adding validation layers we didn't know we needed.

Here's the thing: traditional software has predictable failure modes. A null pointer exception looks the same every time. But LLMs fail in ways that are context-dependent, probabilistic, and often invisible until a real user hits them.

I'm not talking about prompt engineering tips or model selection. I'm talking about the unglamorous reality of putting an AI product in front of actual paying customers.

Edge cases don't follow a distribution curve

In normal software, you can predict edge cases. If you're building a payment flow, you know to handle declined cards, network timeouts, duplicate submissions. There's a pattern.

With LLMs, edge cases are fundamentally different. They're not rare events at the tail of a distribution. They're unpredictable interactions between your prompt, the user's input, and the model's training data.

At Sonic Linker, we built a feature to extract structured data from unstructured text. Worked great in testing. Worked great with our first 50 users. Then someone uploaded a document with tables formatted as ASCII art. The LLM tried to be helpful and invented relationships between data points that didn't exist.

We couldn't have predicted that. And here's the kicker: we still find new variations of this problem every few weeks.

The solution wasn't better prompts. It was building a validation layer that checked if the output made logical sense given the input. If the user uploaded 10 rows of data, the output better have exactly 10 rows. If a field is supposed to be a date, we validate it's actually a date, not the LLM's creative interpretation of one.

This added three weeks to our timeline. But it's the difference between a demo and a product people trust with real work.

You need a human-in-the-loop escape hatch, always

I used to think the goal was full automation. The LLM does the thing, the user gets the result, everyone's happy.

That's not how it works in practice.

We added a simple feature: a "flag for review" button next to every AI-generated output. Sounds obvious, right? But here's what I learned: users don't want to use it. They want to trust the AI completely.

So we made the system flag itself. If the confidence score drops below a threshold, or if the output doesn't pass our validation checks, we automatically mark it as "needs review" and show the user why.

This changed everything. Instead of users discovering errors after they've already used the output (and lost trust), they see upfront that this specific result might need a second look. They're involved in the process, not surprised by the failure.

At Finvestfx, I worked with enterprise clients who needed 100% accuracy for compliance reasons. If I'd tried to ship an AI feature there without an escape hatch, it would have been dead on arrival.

Testing LLM products is a different game

You can't write unit tests the way you're used to. The same input doesn't always produce the same output. And small changes to your prompt can have cascading effects you won't see until production.

Here's what actually worked for us:

We built a test suite of real user inputs, not synthetic examples. Every time we found a new edge case in production, it went into the test suite. We run the entire suite before every deploy, but we don't expect perfect consistency. Instead, we check if the outputs are "semantically equivalent" to the expected result.

We also version our prompts like code. Every change gets a version number. If we need to roll back, we can. This saved us twice when a prompt optimization improved one use case but broke another.

And we monitor everything. Token usage, latency, error rates, but also user corrections. If users are consistently editing the AI's output in the same way, that's an edge case we need to handle systematically.

The takeaway nobody wants to hear

Building with LLMs means accepting that your product will never be 100% reliable in the traditional sense. The question isn't "how do I eliminate edge cases?" It's "how do I design around inevitable unpredictability?"

You need validation layers, escape hatches, and monitoring that goes beyond traditional metrics. You need to be okay with shipping something that works 95% of the time and having a plan for the other 5%.

The AI gold rush makes it look easy. Plug in an API, write a prompt, ship a product. But the real work starts after your first 100 users, when the edge cases start rolling in and you realize this isn't like any software you've built before.