Is that expert commentary genuine, or a quick AI summary? Learn the 7 secret signs—from bland answers to hallucinations—that expose an unsophisticated chatbot and save your sanity.
One of the biggest compliments we often receive from our customers here at SalesAPE ai HQ is that their customers didn’t know they were talking to an AI Agent. Obviously we’re immensely proud of our AI Sales Agents, we put a lot of time, effort and training into them and we hope we don’t sound too arrogant when we say, they’re practically impossible to differentiate from human sales staff.
However, to get our AI Agents this perfect, we spend a lot of time researching and talking to alternate AI technology. And let’s be honest, some of these other AI tools are like trying to talk to a ten year old Amazon Alexa - they really are awful.
While these Large Language Models (LLMs) are incredible tools, sometimes you need to know if you’re speaking with a person or a sophisticated algorithm. Is your bank being helpful, or is it running you in circles? Is that "expert" commentary genuine, or a quick AI summary?
Here are seven secret signs—the subtle tells and glitches—that can help you spot a sneaky chatbot and get the real answers you need.
One of the most immediate giveaways is the tone. Human language is messy. It has digressions, slang, filler words ("um," "like," "so..."), and the occasional typo.
The AI Tell: Chatbots, particularly those trained on vast, general datasets, tend to be overly formal, structured, and syntactically flawless. They avoid emotional language (unless specifically prompted) and produce responses that are factually accurate but lack any unique personality or flair. They are polite to a fault, often starting every reply with a variant of "Thank you for reaching out."
Modern LLMs struggle with immediate, sequential context outside of their current, active chat window.
The AI Tell: If you ask the chatbot to reference something you just said one minute ago, or ask about a real-world event that happened after their last training update (often months or years in the past), they may get stuck or hallucinate. A human agent will remember the last three points of your discussion perfectly. A bot, if not perfectly configured, might forget the nuance of the conversation history.
Pro Tip: Try asking, "Based on my second question, what should I do next?" A human finds this easy; a poorly optimized bot may struggle to retrieve that specific index.
While LLMs are excellent at synthesizing data, they sometimes fall into repetitive loops, particularly when generating content that needs to hit certain keywords or themes.
The AI Tell: Look for the same key phrase or structural element being repeated across multiple sentences or paragraphs. If a piece of writing uses a slightly awkward phrasing, and then uses that exact same awkward phrasing again four sentences later, it's often a sign the model is stuck in its own pattern recognition.
When a human doesn't know the answer, they usually say, "I'll look that up," or "I don't know." When an older-generation chatbot doesn't know the answer, it tends to do something different.
The AI Tell: Instead of admitting ignorance, the bot will often launch into an excessive amount of tangentially related information. It’s trying to overwhelm you with relevant keywords hoping that one of them hits the mark. The response is long, dense, and feels like reading a Wikipedia page that hasn't been properly summarized.
This is the most dangerous and well-known sign, recognized by experts as the biggest flaw in current models.
The AI Tell: LLMs do not "know" facts; they predict the next most probable word based on their training data. Sometimes, that prediction is confidently wrong. If you see a response that is stated with absolute certainty, but includes specific data points (dates, names, statistics) that you know are false, it's likely a hallucination.
Citing Credibility: To address this, many organizations, like Google and OpenAI, are now using retrieval-augmented generation (RAG) frameworks to link the model to verified, up-to-date data sources to minimize these errors. If a bot can cite exactly where it got the information, it’s probably well-integrated; if it just asserts it as fact, be suspicious.
Chatbots are built with extensive guardrails designed to prevent them from producing harmful, illegal, or biased content. Humans, for better or worse, are much more flexible.
The AI Tell: A human customer service agent might bend a rule or try an unconventional solution. A chatbot, when faced with a query that hits a safety trigger, will produce a hard refusal that is often generic and apologetic: "I cannot fulfill this request," or "I am only programmed to discuss topics related to X." This hard compliance boundary is a sign of pre-programmed limitations.
While AI is brilliant at text, simple arithmetic can sometimes trip up pure LLMs (unless they have been specifically integrated with a calculator or code interpreter).
The AI Tell: If you ask the chatbot a complicated multi-step math problem that involves percentages, large numbers, and division, and it takes a long time to answer or provides a nonsensical result, it’s often a giveaway. The language-based model struggles to hold the calculation in memory and accurately process the numbers compared to a standard calculator or a highly focused human.
Spotting a chatbot isn't about defeating AI; it's about being a sophisticated user of technology.
Most of the time, your chatbot conversation will be super productive, maybe even more accurate than talking to a human. But, knowing what to look out for and how to deal with suspicious responses can only be a good skill to have as we integrate AI more and more with everyday life.
Absolutely. As models like GPT-4 and beyond are fine-tuned, they are specifically trained to eliminate these "tells"—especially the blandness, poor context retention, and redundancy. Our own SalesApe Agents, for instance, are trained to exhibit conversational variance, emotional nuance, and flawless context tracking, which makes them practically indistinguishable from a human sales representative. These tells are most obvious in poorly implemented or older, unspecialized AI systems.
Generally, no, as long as you are doing so within the terms of service. Most commercial chatbots are built with extensive guardrails to prevent users from engaging in harmful or illegal activities. Experimenting with creative or complicated queries is a great way to test a bot's limits (especially its compliance wall or mathematical muddle), which can help you understand its capabilities better. Just be respectful and stay within legal boundaries.
A human agent is usually the escalation point when a bot fails to resolve a query or hits a compliance boundary. The fastest method is often a direct, clear statement: "I need to speak to a human representative," or "I need to escalate this to a live agent now." Repeating your request clearly, or asking a question that requires real-time emotional judgment or outside-the-box thinking (like a mathematical puzzle or an emotional dilemma), can often force the system to route you to a person.
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