ChatGPT And Other LLMs Produce Bull Excrement, Not Hallucinations

Let’s begin with a mildly impolite but useful question: when ChatGPT or another large language model confidently invents a fact, is it really “hallucinating”? That word sounds dramatic, almost poetic. It makes the machine seem like a troubled genius staring into the digital void and seeing things that are not there. Very cinematic. Also very misleading.

A better description, many critics argue, is much less glamorous: these systems often produce fluent, polished, truth-indifferent nonsense. In everyday English, that is bull excrement. In philosophical language, it is output generated with little built-in concern for whether it is actually true. The difference matters because the label shapes how people use the tool, how companies market it, and how readers trust it.

If you call the problem a hallucination, you risk flattering the machine. You make it sound like it has perception, inner experience, or a broken relationship with reality. But language models do not perceive the world the way humans do. They do not look, listen, or remember in the ordinary sense. They generate text by predicting likely sequences of words based on patterns in training data and prompt context. That design can be incredibly useful. It can also be spectacularly wrong while sounding like it deserves a PhD and a corner office.

Why “Hallucination” Is a Convenient but Slippery Word

The tech industry likes metaphors. It gives software “agents,” “brains,” and “reasoning.” The word “hallucination” fits neatly into that tradition. It is sticky, memorable, and media-friendly. The problem is that it smuggles in the idea that the model was trying to report reality but somehow misperceived it. That is not what is happening.

LLMs Do Not Mis-see the World

When a human hallucinates, the concept implies an experience: seeing a dog that is not there, hearing music in an empty room, mistaking fantasy for sensory reality. An LLM is not doing that. It is not having an experience at all. It is generating a statistically plausible continuation of text. That continuation may be right, wrong, half-right, or dressed like the truth while quietly sneaking out the back door with the silverware.

This matters because people hear “hallucination” and imagine a rare glitch. In reality, fabricated output is not a bug that appears only on unlucky Tuesdays. It is a structural risk of systems designed to produce the next likely token, especially when they are asked for an answer instead of being allowed to say, “I don’t know.”

Fluency Tricks Humans Into Trust

What makes LLM errors dangerous is not merely that they happen. It is that they happen elegantly. The model does not usually cough, shrug, and admit confusion. It often delivers a smooth paragraph, a confident citation, a professional tone, and the unmistakable vibe of a student who did not read the book but still volunteered to lead the discussion.

That fluency encourages overtrust. Readers often assume that a polished answer must reflect knowledge. But style is not evidence. Confidence is not verification. Footnotes are not facts if the footnotes themselves were invented five seconds ago by predictive text wearing a tie.

Why “Bull Excrement” May Be the More Honest Label

The provocative argument behind calling LLM output “bull excrement” is not simply that the machines are wrong. Humans are wrong all the time too. The stronger claim is that these systems are not oriented toward truth in the first place. Their core job is to produce plausible language. Truth can emerge from that process, but it is not guaranteed by it.

That distinction is powerful. A lie is intentionally false. Hallucination suggests distorted perception. Bull excrement, by contrast, refers to speech produced without adequate concern for whether it is true or false. And that maps uncomfortably well onto many chatbot failures. The model does not need malicious intent. It only needs to keep generating likely words even when the factual foundation is missing, thin, or entirely imaginary.

In other words, the model is not always “trying to deceive.” It is trying to complete the assignment. Unfortunately, when the assignment is “answer this confidently,” the machine may choose smoothness over uncertainty. That is not wickedness. It is optimization wearing bad judgment.

What Real-World Failures Reveal

This is not an abstract debate for philosophers and people who alphabetize their spice racks for fun. The language we use affects practical decisions. When leaders treat fabricated output like a charming eccentricity instead of a design risk, bad things follow.

Law, the Place Where Made-Up Cases Are Extra Unpopular

Legal examples have become the industry’s cautionary campfire story for a reason. Courts have seen filings that included nonexistent cases and fake citations generated with AI assistance. Judges, perhaps unsurprisingly, were not delighted. These incidents exposed a brutal truth: an LLM can generate something that looks exactly like legal research while being closer to improv comedy than jurisprudence.

If we describe that as a hallucination, it sounds almost accidental and mysterious. If we describe it as truth-indifferent text generation, the lesson becomes clearer: never outsource factual authority to a system that can improvise a citation with the confidence of a witness who definitely was not there.

Search, City Services, and Other Places Where Accuracy Is Not Optional

Public-facing AI tools have also produced bad guidance about regulations, current events, and basic factual matters. Search chat features and government-adjacent bots have offered answers that sounded helpful while being flatly wrong. That is not a tiny marketing problem. It is a trust problem. If a user asks about laws, permits, medicine, or money, a stylish wrong answer is worse than a clumsy non-answer.

Put differently, users can survive a slow tool. They can survive a boring tool. What they cannot safely survive at scale is a tool that speaks like an expert while free-styling the truth.

Why LLMs Produce This Stuff in the First Place

The obvious joke is that the model is lazy. The less funny answer is that the incentives are messy.

They Are Trained to Continue, Not to Know

At a basic level, LLMs learn patterns in text. They become very good at predicting what kind of phrase should come next. That makes them excellent at drafting, summarizing, reformulating, translating, and mimicking structure. But pattern completion is not the same thing as grounded knowledge.

If the model has enough related examples in training data, it may generate a strong answer. If it has fragments, outdated associations, or ambiguous cues, it may still produce an answer anyway because that is what the system is optimized to do. The machine is rewarded for continuation. Silence is not its favorite hobby.

Benchmarks Can Reward Guessing

Another issue is evaluation. If developers mostly reward models for giving an answer rather than accurately signaling uncertainty, the safest-looking behavior may actually be risky behavior. A model that always takes a swing can appear more capable on simple scoreboards than a model that cautiously abstains. Humans call that overconfidence. Product teams sometimes call it “great user experience” right until it invents a regulation, a source, or a legal precedent.

Grounding Is Often Missing

When an LLM answers from its general patterns alone, it can drift. That is why grounded systems matter. If the model is tied to trustworthy documents, databases, search results, or retrieved enterprise content, the output has a better shot at staying anchored. Think of grounding as taking the chatbot away from the improv stage and handing it a binder full of notes with highlighted passages.

Grounding does not make a system magically perfect, but it does shrink the room available for invention. And in high-stakes settings, shrinking that room is a wonderful idea.

How to Use LLMs Without Getting Professionally Embarrassed

Here is the practical middle ground: you do not need to treat LLMs like fraudulent fortune tellers, and you absolutely should not treat them like omniscient interns from the future.

Use Them for Drafting, Not Final Authority

LLMs are often strong at first drafts, outlines, rewrite options, brainstorming angles, tone shifts, and summarizing material you already trust. They can help you move from blank page paralysis to a usable starting point. That is real value.

But the moment the task requires factual authority, current information, legal precision, medical reliability, or citation integrity, the rules change. Verification becomes mandatory. The model can assist the process; it cannot be the process.

Ask for Evidence and Uncertainty

Good prompt design helps. Ask the model to distinguish facts from assumptions. Ask it to say when it is uncertain. Ask it to quote from source material if source material is provided. Ask it not to invent citations. These moves do not eliminate fabricated output, but they improve your odds of catching it before it catches you.

Design for Refusal, Not Just Completion

Many teams still optimize chatbots to always be helpful, always respond, and always sound polished. That sounds customer-friendly until the bot becomes helpful in the way a random stranger on the internet is “helpful” while explaining tax law. A better design philosophy makes room for refusal, uncertainty, clarification, and retrieval from trusted sources. Sometimes the smartest answer a model can give is, “I can’t verify that from the information available.”

So, Is the Term “Hallucination” Useless?

Not completely. It is widely understood, easy to recognize, and already built into industry vocabulary. Many standards bodies, researchers, and companies still use it as a shorthand. But shorthand can conceal as much as it reveals. The trouble is not just that the word is imperfect. It nudges people toward the wrong mental model.

It suggests the machine almost had the truth, as though reality slipped through its robotic fingers. Often the better explanation is more mundane: the model produced a plausible sentence because plausible sentences are what it does. Truth was optional. Fluency was not.

That is why the harsher label has value. “Bull excrement” shocks readers into seeing the actual risk: not magical machine madness, but polished output that can float free from evidence while sounding perfectly composed. It reminds users that a chatbot’s sentence is not a window into reality. It is a generated artifact that may or may not deserve trust.

The Better Rule for Readers

If you remember only one thing, let it be this: do not ask whether the model sounds right. Ask whether the claim is grounded, verifiable, and sourced. A chatbot can be useful, fast, funny, and even impressively insightful. It can also be confidently wrong in complete sentences with excellent punctuation.

That is the modern hazard. Not broken grammar. Not obvious nonsense. Not robot noises. The hazard is competence theater. The hazard is believable prose detached from reliable evidence. Call it hallucination if you like. But if you want a term that keeps your feet on the ground, “bull excrement” may do the job better.

Because once you see the problem clearly, you start using these tools more wisely: as assistants to checked knowledge, not replacements for it. And that, in the age of fluent machines, is a distinction worth stapling to the wall.

Experiences People Commonly Have With This Problem

The most revealing part of the LLM debate is not always in research papers or product announcements. It is in the ordinary moment when a person realizes, “Wow, this thing sounds extremely sure for a machine that just made up a detail from thin air.” That moment arrives in many flavors.

A student might ask for help with a history assignment and get a beautifully organized answer complete with dates, causes, and a quote that never existed. At first it feels magical. Then the student checks the source and discovers the quote is fiction wearing spectacles. The experience is strangely educational: the tool did not merely fail; it demonstrated how persuasive bad information can become when wrapped in crisp prose.

A developer may ask for code, paste it into a project, and watch it reference a function that is not in the library version being used. The code looks clean. The comments are polished. The bug report arrives anyway, usually with emotional fireworks. The developer learns the hard way that a model can imitate documentation style better than it can guarantee documentation truth.

A marketer might use an LLM to draft campaign copy about a product line and receive claims that sound compelling but were never approved by legal, never supported by product specs, and never blessed by reality. Suddenly the team is not saving time; it is spending time cleaning up synthetic overconfidence in a blazer.

A manager may ask for a summary of a long meeting transcript and get something mostly correct with two invented action items and one imaginary consensus that no human in the room actually shared. This is one of the sneakiest failure modes because it feels useful enough to trust. The errors are not cartoonishly wrong. They are socially plausible, which makes them more dangerous.

Even casual users run into the same pattern. Ask a chatbot for travel advice, a health explanation, or help comparing services, and the answer may sound wonderfully organized. Bullet points. Tone. Confidence. Maybe even fake precision. The trap is that structure feels like evidence. It is not. Anyone who has ever been confidently given wrong directions by a person in a parking lot already understands the human version of this problem. LLMs simply industrialize it.

These experiences are why the wording debate matters. “Hallucination” can make the failure sound rare, exotic, or almost human. But what people actually experience is something more ordinary and more important: a system that can generate polished language faster than it can guarantee factual alignment. Once users grasp that, they often become much smarter and calmer. They stop worshipping the machine, stop panicking about the machine, and start checking the machine. That is probably the healthiest relationship available.

Conclusion

Large language models are not useless, and they are not mystical. They are powerful text engines with uneven loyalty to truth. Calling every fabricated answer a “hallucination” can obscure the central issue: these systems are often rewarded for sounding good before they are rewarded for being right. If we want safer, more trustworthy AI, we need language that makes the risk plain, product design that values groundedness, and users who verify before they trust. The machines can help a lot. They just do not get to wear the crown of authority without receipts.