Why AI Sounds Like an Expert and How to Make it Act Like One Too

Recently, a viral trend appeared on social media. When you ask ChatGPT, "Is there a seahorse emoji?", it can fall into an infinite spiral loop. It first answers that yes, there is a seahorse emoji, provides an emoji, and then immediately takes it back, saying that it gave the wrong emoji and that this one below is the correct one. Then, it apologizes again and repeats the same process. This loop can go on endlessly until a hard stop is triggered after the model exceeds its maximum token limit for a single response.

It's a funny glitch, but also a fascinating phenomenon. In this article, I'll try to explain why large language models (LLMs) have such a hard time simply answering "I don't know", "I don't have this information", or "I can't do that" and instead produce hallucinated, weird, or basically wrong information.

AI chatbots such as ChatGPT, Gemini, and Grok sound like experts but don't think like them. They're good with language, not logic. These models don't calculate or verify facts in the human sense — they predict the next likely word or number based on patterns they've learned from text. In other words, they don't reason; they approximate what an expert would probably say, based mainly on probability.

That's also why they answer confidently even when they're wrong. The model's training process rewards coherence and completeness, not honesty or uncertainty. Saying "I don't know" is statistically rare in the data they're trained on, so they learn to avoid it. Ironically, it's even harder to avoid once the model is bigger (so theoretically — smarter). To a language model, a confident guess looks more correct than an admitting lack of knowledge, even when the guess is absurd.

We ran a short survey asking users how much they trust AI chatbots and how satisfied they are with their answers. Below are the results across four key areas: trust, accuracy, ease of use, and clarity of explanation.

Most users were generally happy with AI results, but trust and clarity scored lower than other aspects. This shows that while AI sounds confident, people still doubt how much it actually knows.

That receptionist is a lot like a modern chatbot. When you prompt, for example, ChatGPT, the big language model (the LLM) behind the interface acts as the front desk — it listens to what the user wants, tries to interpret the intent, and then — if the request is too complex for the receptionist to answer — decides which smaller, specialized system should handle the request. Those systems, the "departments", could be math modules, code interpreters, search APIs, or physics engines. Each one is designed for a specific type of problem, while the main model focuses on understanding the question and routing it correctly.

The challenge is that this "receptionist" role is harder than it sounds. Just like a real receptionist can misunderstand a vague question ("I want something with numbers"), an LLM can misread a prompt and send it to the wrong expert. In AI terms, that's called task routing, and it's one of the biggest problems to solve. If the main model misclassifies the user's intent, it might try to do everything itself, and that's when it starts to hallucinate or give inconsistent answers.

This structure mirrors what's happening in new Mixture of Experts (MoE) models. In those systems, a large "gating network" acts like our receptionist, deciding which small expert models to activate for each input. Only a few experts are used at once, saving computation and improving accuracy. Some chatbots now combine this internal routing with external tools, so when the model spots a math or finance problem, it can automatically call a calculator or verified database instead of trying to guess the answer.

In short, the receptionist doesn't need to know everything, but they do have to know their limits and when it's best to leave the matter to someone else rather than risk giving the client wrong information.

The smarter our "receptionist" gets at understanding and routing problems, the closer we get to chatbots that not only sound intelligent but actually deliver expert-level answers every single time.

There's a common mix-up worth clearing up — chatbots and LLMs aren't the same thing.

A large language model (LLM) is the brain — a raw generative engine trained to predict text based on patterns it has seen. It doesn't have memory, access to the internet, or real-world data. On its own, it's just a probability machine that produces the most likely next word or number.

A chatbot, on the other hand, is the whole product. It gives that brain a personality, interface, memory, safety rules, and, increasingly, access to external tools and APIs. That means a chatbot isn't just a talking model, but a system that can decide when to ask for help from other programs.

This distinction matters more than ever because chatbots can now be upgraded with capabilities that plain LLMs don't have. One of the most significant parts of that shift is function calling — a mechanism that lets the LLM delegate work to specialized tools. Instead of trying to guess an answer, the chatbot can recognize intent ("calculate compound interest", "fetch today's EUR/USD rate", or "summarize this PDF") and hand the task off to a designed-for-the-task service that has a high probability of doing it correctly. The LLM interprets your request, formats it as structured data (usually JSON), and then an external engine executes the actual operation (a calculator, search API, code interpreter, etc.).

We're already seeing this in action across the industry.

• Perplexity, for example, has built its reputation on combining an LLM interface with live search and verified citations. It doesn't just predict answers — it retrieves them from the web in real time and cites the source inline, something base models can't do or at least struggle to do.

• OpenAI's ChatGPT now uses the same principle through its "function calling" and "Actions" features. When you ask for the weather, it can call a weather API. When you upload a CSV, it runs Python code in a sandbox to analyze it. The main model (our receptionist) itself isn't doing the math or pulling the data; it's delegating to other systems.

• Anthropic's Claude follows a similar architecture: the LLM provides context understanding and conversational fluency, while connected plugins and internal APIs handle live data, code execution, and document retrieval.

This hybrid design has become the new standard because it balances fluency with reliability. Chatbots built this way no longer rely on probabilistic guesses alone. They're supposed to know when to delegate, and they can integrate the verified output from a specialized engine back into the conversation.

You can clearly see the problem with LLMs giving information that can't be verified when you use voice mode in different chatbots. People often report that the voice versions are less capable and more likely to give wrong answers, and I can confirm that from my own experience as an everyday user of ChatGPT.

In a few conversations, it gave me precise information — for example, the average price of a car I was looking to buy at the time. Because I'm passionate about cars and have spent hundreds of hours on car auction websites, I was competent enough to realize the answer isn't correct. So I asked for the source, hoping to understand what data it was basing its response on.

It couldn't provide any source, no matter how many times I asked or how specific I was about needing exact data points used for giving me a very specific price range. It kept repeating that its answer was based on general knowledge of car auctions and what's available on the market, but it was completely off. That experience perfectly shows why humans, especially experts, are still essential in the loop. If you're not an expert and you ask an AI chatbot for information you can't verify yourself, you're forced to either trust it unquestioningly or double-check it elsewhere.

Because I know cars, I caught that the information was wrong, but how many other users get incorrect answers without realizing it? And how much inaccurate information is already being used by people who simply had no way to know it wasn't true?

To see how these AI limitations manifest in practice, I asked two of our experts at Omni Calculator (and probably the smartest people I know) — both researchers with deep technical backgrounds — to share what they've observed in their fields.

Anna Szczepanek, PhD — Mathematician at Jagiellonian University

Anna researches mathematical physics and applied mathematics at the Jagiellonian University in Kraków, and she's behind many of Omni's most precise math and statistics tools. Because Anna works deeply with mathematical tools and numeric behavior, she often sees where LLMs break under real-world scenarios:

"AI chatbots can talk math, they're great at explaining concepts, but they struggle when precision is needed, especially with very large or very small numbers. The root issue is how computers represent numbers: floating-point arithmetic is inherently approximate, and round-off errors propagate. Even well-engineered algorithms in numerical analysis must guard against instability and loss of significance. LLMs struggle with that a lot."

Joanna Śmietańska-Nowak, PhD — Physicist at AGH University of Science and Technology

Joanna teaches and researches physics and materials science, and she's worked on everything from nanotechnology to crystallography. At Omni, she focuses on physics and chemistry calculators, and she knows how much difference the wording of a prompt can make:

"When you work with LLMs, you quickly realize that how you ask a question often matters more than one could think." Joanna explains. "The phrasing of a prompt decides how deeply the model thinks about the problem — whether it gives you a quick, surface-level reply or actually engages the right reasoning pathways. It's a bit like Daniel Kahneman's System 1 and System 2 thinking: if you ask casually, the model reacts instinctively — if you ask precisely, with context and specific instructions, it slows down and reasons. That also affects which internal or external modules the chatbot activates. A simple question might stay within the general language model, while a well-structured prompt can trigger routing to a specific expert model or even a function-calling tool. In physics problems, that difference can mean everything."

These examples make one thing clear — even when AI sounds confident, its accuracy depends entirely on how precisely you frame the problem and how well it handles numbers behind the scenes whether it's math rounding, physics assumptions, or everyday reasoning.

So how do we fix this?

We don’t make LLMs bigger; industry experts concluded that there is a sweet spot in terms of the model’s size. Instead, we make them smarter by pairing them with tools: systems that always return the same, verified result for the same input. Tools that don’t guess, but calculate.

At Omni Calculator, we’ve put a great effort into making sure our calculators perform how they should, and one of our best works is a solution we designed ourselves - Omni Rounding. Rounding may sound like a trivial detail, but in practice, it's one of the most common sources of inconsistency in numerical answers. Modern programming languages, like JavaScript, struggle with floating-point precision. Even numbers like 0.1 + 0.2 can fail to add up to 0.3. On a small scale, the difference looks invisible, but in multi-step calculations, it compounds fast, and based on our research, it's the reason for most AI chatbots' incorrect calculations.

As our Engineering Team Leader, Jarosław Bąk says: “It’s an inelegant solution, but it works”.

Omni Rounding is our own system that bypasses JavaScript's native precision problem. It handles very large and very small numbers reliably, using explicit rounding rules. It enforces the chosen mode consistently across all operations, so every calculator or chatbot that connects to it produces identical, verifiable (and repetitive) results. There's no excess token usage, no random rounding, no confusion, and no inconsistency between sessions.

For AI chatbots, this is a game changer. When connected through function calling, the model would no longer need to simulate arithmetic or write code. It could simply send the parameters to a ready-to-use solution, like Omni Calculator, which could return the clean, exact result in milliseconds. The chatbot could then wrap it in natural language.

Here's why this hybrid setup, LLM + external expert tools, is quickly becoming the best solution and industry standard:

• Cost + Speed — When an LLM generates Python code to calculate something, it must think out loud in text, which costs tokens and time. Each new prompt requires regenerating the logic, running it in a sandbox, and translating the result. A single well-defined API call, however, uses only a few tokens — the function name and parameters, and executes instantly on the backend. It's both faster and far cheaper.
• Reliability — Code execution can vary between environments or fail silently due to floating-point drift, formatting, or version mismatches, whereas dedicated tools run on tested, version-controlled systems. Given the same input, they'll always give the same output.
• Security — Letting an LLM execute arbitrary code is risky because external tools have strict input validation, guardrails, and logs. They expose only the functions you want to allow, nothing else, so you can guarantee safety and traceability.
• Accountability — When an AI pulls data from the internet, you often can't tell where it came from, especially in voice mode. Sometimes it even comes from user-generated sites like Reddit or Quora. With a verified external system, that changes. The chatbot can respond: "This answer comes from Omni's compound interest calculator, version x.x, here's the link" giving the user a clear source to verify or challenge. If a user finds a potential error, they know exactly where to look, and you, as the provider, can check the same reference point. That level of accountability is impossible when the AI's knowledge is a black box of web data or when it provides you with a massive amount of sources, like Deep Research functions do.

External tools like Omni Calculators aren't just efficient plugins for LLMs — they're trustworthy by design. Every calculator in our library is created by domain specialists — scientists, engineers, medical professionals, finance experts, and then proofread and verified by reviewers and native-speakers. Every formula is backed by references, tested, and documented. Each author is listed on the calculator's page, so you know who's responsible, and you, as a user, can decide if you trust it or not. That means any chatbot plugged into these tools isn't just calculating — it's drawing from a credible, expert-reviewed knowledge base.

Using external tools makes a massive difference in practice. When a chatbot uses an LLM alone, its knowledge is whatever it has absorbed from training data, which usually includes everything from peer-reviewed papers to random Reddit threads. When it calls a specific program, a specific solution, like an Omni Calculator, the source is single, authoritative, verifiable, and fully transparent. It's a win on every front — cheaper, faster, safer, smarter.

So to summarize — LLMs talk, but it's the tools that should calculate, and when those tools come from credible, expert-built systems, AI doesn't just sound smart, but acts like it.