If you thought you could set up a simple AI integration and be on your way, I’ve got some news for you.

AI literally does not know everything.

In fact, it doesn’t “know” anything at all. 😅

Large Language Models (LLMs) are driven by probability, not understanding. They generate responses based on what they believe is the most likely next word given the context they’re provided. That’s it. There’s no intuition nor real-world awareness.

Enter hallucinations. If a model doesn’t have clear instructions, constraints, or access to the right data, it will still try to be helpful. And sometimes “helpful” looks like confidently making things up.

Imagine you have an AI-powered chatbot on your website. It hasn’t been told anything about your current promotions or discounts. A user asks, “What discounts do you offer today?”

The model doesn’t know the answer. But it doesn’t respond with “I don’t know” by default.

Instead, it might say something like:

“Sure! Use code FREE99 at checkout.”

That discount doesn’t exist. But from the model’s perspective, it sounds plausible. And plausibility is often enough for an LLM to respond with confidence.

This is why saying “AI lies” isn’t quite accurate.

LLMs don’t lie.

They just don’t know.

Hallucinations usually aren’t a model failure. They’re a system design issue. Vague prompts, missing context, no grounding data, or unclear boundaries all increase the likelihood that a model fills in the gaps on its own.

This is also why AI needs to be treated like any other system component, not magic. If accuracy matters, you have to design for it. That means clear system instructions, constrained outputs, and sometimes accepting that AI is the wrong tool for the job.

AI can be powerful, but only when it’s used intentionally. Otherwise, it will confidently give you an answer whether or not that answer is true.

How teams actually reduce hallucinations

Unfortunately, there’s no silver bullet for hallucinations. If someone tells you they’ve “solved” it, they’re either overselling or misunderstanding the problem. Hallucinations are a byproduct of how LLMs work. The goal isn’t really elimination. It’s reduction, containment, and designing for failure.

The first thing strong teams do is ground the model in reality. If accuracy matters, the model shouldn’t be answering from vibes alone. Teams use retrieval-based approaches so the model can only respond using approved data sources, like product catalogs, internal documentation, or structured databases. If the information isn’t present, the model should not guess. This is referred to as retrieval augmented generation (more on this later!)

Next, teams get very explicit about what the model is allowed to say and when it should stop. This means clearly instructing the system to say “I don’t know” or escalate when it lacks confidence or data. Silence, refusal, or deferring to a human is often safer than a confident but incorrect answer. Typically you’ll have a system level prompt with instructions on what the model can and cannot do when it comes to responding to user queries.

Another important shift is constraining outputs. Open-ended generation increases the chance of creative mistakes. When possible, teams limit responses to predefined formats, structured fields, or controlled vocabularies. Fewer degrees of freedom means fewer opportunities to hallucinate. So, a free form text box probably isn’t always the best way to go. Where you can add in structure – do.

Crucially, teams don’t rely on prompts alone. Guardrails live outside the model. Outputs are validated, logged, and monitored. Responses that look suspicious or fall outside expected patterns can be flagged or blocked before reaching users. Over time, these feedback loops help tighten the system. It’s a good idea to have some sort of observability tool or logging setup so that you can monitor prompts and responses, and tweak the instructions based on user behavior.

The main takeaway: AI is probabilistic, not deterministic. You don’t design these systems assuming they’ll always be right. You design them assuming they’ll occasionally fail. If a hallucination would cause real harm, there needs to be a fallback, a human in the loop, or a hard stop.

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Thinking about using AI in your product? I offer focused working sessions to help founders decide what’s worth building and what isn’t. Check it out here: LINK.