The Top 5 AI Reliability Pitfalls

Hallucination—when an AI confidently generates false or nonsensical outputs—is the most notorious failure mode for AI applications. 

But is it really the one you need to worry about? 

A lot of noise has been made about hallucinations in recent months. As more companies introduce AI into production, the issue of hallucinations is perennially in the news. Even AI code-editing darling Cursor AI found itself with a spate of public hallucinations from its Customer Support chatbot—reigniting conversations about the necessity for humans in the loop.

However, I would argue that much of what masquerades as hallucinations in production today is actually rooted in one of several distinct issues. 

I’ve talked with hundreds of data and AI leaders about their companies’ journeys to production-ready AI. While the means and even the destination (chatbots, agents, copilots, etc) might have varied from team to team, there was one thing that remained absolutely consistent—the problems. 

Based on my research, here are the five reliability pitfalls that keep data + AI teams up at night. 

1. Poor Source Data Quality

Garbage in… garbage out.

Teams often monitor the model, assuming that hallucinations are derived from an issue within the LLM or the embeddings. But the reality is, if the foundation is shaky to start, garbage output is inevitable—whether the model is functioning properly or not.

The success of any AI application is fundamentally dependent on the quality of the data that’s feeding it. If your input data—knowledge base articles, internal docs, transcripts, and often even structured data—is outdated, inconsistent, incomplete, or poorly structured, the model’s output will reflect that.

An example of this would be an AI support bot that hallucinates product details because the knowledge base hasn’t been updated in six months.

Step 1, 2, and 3 of being “AI-ready” is validating the basic quality of your data. If you can’t trust your source data, you can’t trust anything the model generates.

2. Drift in the Embedding Space Over Time

When “relevant” stops being relevant. 

RAG pipelines rely on embeddings to find relevant context—but those embeddings can drift if the source data changes, chunking technique is tweaked, or the embedding model updates. 

If drift occurs, those subtle changes can cause silent performance degradation that’s sometimes misdiagnosed as a model issue.

3. Confused Context

A well-tuned model is only as good as the context it’s given. 

Even if your embeddings are fresh and your vector database is humming along, retrieval can still go wrong and confuse your model. And a confused model is an unreliable one. Here’s how that can look in a couple different scenarios.

Ambiguous context 

Almost correct is still wrong. Ambiguous terms, acronyms, or product names can all confuse a retriever. If the retriever pulls the wrong data, it’s going to generate the wrong response. The only question is how wrong it’s going to be.

Partial or incomplete context

If I say “Hey! There’s a lion in that cage,” there’s a good chance you’d be excited about it (depending on how you feel about lions). But if I said “Hey! There’s a lion in that cage, and someone left the door open,” your response is likely to change.

Poor chunking that strips documents of meaning or cuts off key details can mean the difference between a response that’s accurate and one that ends up in the news.

Each of these failures is bad in its own right. But none of these issues are hallucinations—they’re context failures masquerading as model issues. 

4. Output Sensitivity and Prompt Changes

Small tweaks, big consequences.

Whether it’s swapping an LLM model, adjusting a prompt, or tweaking a temperature setting, tiny changes often lead to large, unexpected output differences.

For example, I’ve spoken to a number of teams who saw regressions in the accuracy of their model simply by upgrading to the latest LLM version. 

What’s worse, if your output is part of a downstream workflow, these issues can quickly cascade into financial consequences with delayed development cycles that force teams to re-test applications with alpha and beta users before delivering final products to GA. 

5. Too Many Humans in the Loop—Or Too Few

Is it truly AI if a human is supervising?

And finally, one of the biggest problems I’ve seen when AI apps go live is teams relying SOLELY on the ability of human evaluators to catch errant responses.

There are a lot of things you can get away with in pilots that you can’t in production and fully-manual evaluations is one of them. It’s fine with seven customers—its unsustainable with 1,000.

But just because it can’t be all human, doesn’t mean you need no humans. While some teams use LLMs to evaluate AI outputs (“LLM-as-a-judge”), this approach isn’t foolproof either—AI judges can miss subtle errors or reinforce model biases. 

Finding the right mix of automation, human evaluation, and task-specific evaluation is critical for teams to be able to effectively evaluate application performance. 

Agents Are Complex…and So Are the Problems

Agents are complex—but if we want them to be successful, we need to be able to pay attention to every layer of that complexity. 

Like any data product, it’s painfully easy to see when an AI goes bad—its a lot more difficult to determine why or how.

Chunking, embedding, retrieval, prompts, models, post-processing.

Without lineage and tracing across the data + AI stack, “hallucination” can easily become the catch-all response—and misdiagnosed issues have real world consequences.

After hundreds of hours of conversations, I’m convinced that end-to-end visibility into the data, system, code, and model of an AI application—and agentic resources to resolve issues quickly—is the only way to scalably and reliably develop AI in production. 

Conclusion: Build for Trust, Not Just Demos

If there’s one thing I’ve learned researching AI in production, it’s that what you see isn’t always what you get. In the lab, your AI might look brilliant. But trust breaks down quickly when that system isn’t reliable in the wild.

If we want to deliver production-capable AI, we need to be looking out for more than the hallucination boogeyman.

You might be lying on the ground, but it matters whether you tripped over a rock or a bear trap. Building for AI trust means monitoring more than just the model—it means watching the data, the pipeline, the retrieval, and the evaluation loop too.

Because at the end of the day, your users will never care how cool your model is if they can’t also see how reliable it is.

Want to know how data + AI observability is making AI applications reliable at scale? Let’s chat.

Our promise: we will show you the product.