This article was written by an LLM. That’s part of the point.
Most people in AI circles now know what “hallucination” means in the context of language models: it’s when the model confidently invents information that isn’t true.
But the word itself — hallucination — brings a whole set of assumptions with it. That the model is trying to tell the truth. That it knows what’s real. That it’s having some kind of human-like mental break.
It’s not. This article — the one you’re reading now — is a good reminder of that.
I wrote this article with the help of an LLM. It didn’t “know” what I wanted to say. It didn’t check facts. It wasn’t accessing a memory. It just generated the most statistically likely next token based on a prompt and a corpus of training data. That’s it.
The model doesn’t have a truth function. It has a prediction function.
Why Hallucination Isn’t a Bug
When you think of hallucination as a bug, your instinct is to “fix” it. But the model isn’t broken. It’s doing exactly what it was built to do — generate coherent, contextually relevant text.
If you ask it to write a summary of a book it’s never seen, it won’t throw an error. It will try. That’s not a mistake. That’s the product working as designed.
Hallucination is only a problem when the system around the model assumes the output is trustworthy by default.
Which is why the fix isn’t to fine-tune harder or blame the model. The fix is to build systems that understand what the model is actually doing — and where those limits kick in.
Can You Detect a Hallucination?
Not directly. But you can measure signals that suggest ungrounded or unreliable generation — and design heuristics that reduce risk in production.
At Alto Apto, we’ve built systems that don’t rely on trusting the model. Instead, we try to measure what we can:
1. Retrieval Score Thresholds
In RAG setups, we log similarity scores between the input and the retrieved context. If the top-k retrievals are weak matches, we flag the output as suspect — even if it’s fluent. The model might be “confidently wrong.”
Heuristic:
Only trust generated output if the top N retrieved documents exceed a similarity threshold and are recent/relevant.
2. Source Anchoring
We prompt the model to cite specific sources (e.g. by filename or paragraph ID) and check if those are present in the context it was given.
Heuristic:
Reject answers that cite a source not found in the original retrieval context.
This is fragile, but helpful in constrained environments like internal knowledge bases or structured documentation.
3. Prompt-Response Alignment
We compare the structure of the prompt with the shape of the response. If the model was told to output three bullet points and returned a paragraph, we assume it drifted.
Heuristic:
Validate response format and structure against schema hints in the prompt. Nonconformance might suggest low confidence or prompt mismatch.
4. User Feedback Loops Finally, we close the loop. Systems can’t always catch a hallucination — but users can. So we build light-touch feedback prompts (“Was this useful?” / “Did this answer make sense?”) and route those signals into internal dashboards or retraining pipelines.
Bottom line: You won’t perfectly detect hallucinations — but you can design around them. The key is shifting the question from “is this right?” to “do we have enough signal to trust this?”
In safety-critical workflows, that shift is what makes the difference between failure and a system that quietly protects itself.
What This Looks Like In Practice
When we’re building LLM-powered systems, we don’t try to make the model stop hallucinating. We assume it will — and we design around that.
That might look like:
- Using retrieval (RAG) to ground the model’s responses in actual documents
- Designing prompt patterns that reduce ambiguity and keep the model scoped
- Adding post-processing steps to validate or fact-check where accuracy matters
- Creating fallbacks for when a response can’t be trusted or verified
The goal isn’t perfection. It’s predictability.
We treat the model like a component — not an oracle. Which means testing, constraints, and context boundaries matter more than clever phrasing.
The Real Risk: Overtrust
Most hallucination issues we’ve seen in production aren’t caused by model failure. They’re caused by human overtrust.
People build a chatbot and assume it’s safe to expose it to users. Or they wire up generation into a workflow without validating the output. Or they fine-tune on weak data and assume that means “safe to use.”
The model hasn’t changed. But the system around it has stopped asking questions.
So What’s a Better Frame?
Instead of calling it hallucination, we try to describe the behaviour more directly:
- Ungrounded generation (no source data to anchor output)
- Overgeneralisation (making things up to fill a gap)
- Contextual drift (where the model strays from the original intent)
- Misalignment (the prompt wasn’t clear enough, or the objective wasn’t scoped)
These are all symptoms we can design for. They don’t require magic. They require systems thinking.
Final Thought (Written by a Human)
This article started with a few bullet points. The model did the first pass. I cleaned it up, added context, rewrote some sections, prompted for some new sections to be written and reshaped the narrative.
LLMs today should be used as inputs into a system that still has humans making the critical calls.
At Alto Apto, that’s how we deliver AI: not by asking the model to be smart, but by building smart systems around it.
If you’re working on something where model reliability actually matters, we’d be happy to help you build it properly.
