- ChatGPT generates text based on probability, not truth, causing hallucinations and errors even when it sounds very convincing.
- The most common errors include fabricated data, loss of context, tone problems, biases, and limitations of recent knowledge.
- The way in which questions are asked greatly influences the quality of the answer; vague prompts, without context or examples, multiply errors.
- Verification with reliable sources, human review, and style adaptation are key to using ChatGPT safely in professional environments.
Since its emergence in late 2022, ChatGPT has become the go-to tool for writing messages, resolving doubts, and getting help with technical tasks, but its massive success coexists with an uncomfortable reality: it's not infallible; it makes mistakes, and sometimes it does so with excessive certainty . Millions of people already use it in Spain, although data shows that only a small percentage use it daily, largely because it hasn't quite generated the level of trust one might expect.
Many users perceive that, while very useful, ChatGPT sometimes fabricates data, contradicts itself, misinterprets context, or responds as if it knows more than it actually does . This is compounded by other practical flaws: service overload, rapid changes of opinion, difficulty understanding irony, and problems with mathematical or specialized reasoning. Understanding these limitations is key to using it effectively without falling into the trap of thinking, "If the AI says so, it must be true."
Why ChatGPT is wrong even though it sounds so convincing
The first thing to understand is that ChatGPT isn't designed to "search for the truth," but rather to predict words . It's a language model that, based on everything it has seen during its training, calculates which combination of terms is most likely to fit your question. This means it prioritizes fluency and apparent coherence, not rigorous fact-checking. If you're interested in seeing comparisons between similar models, check out the differences between Claude, ChatGPT, and Gemini.
This way of operating causes the infamous AI "hallucinations"—that is, fabricated responses that sound remarkably sound . It can cite nonexistent studies, mention laws that were never passed, or detail entirely fictitious historical cases, all with such confidence that it's hard to question it. From the outside, it sounds like an encyclopedia, but internally it's simply extrapolating linguistic patterns.
Experts analyzing these models insist that we must avoid anthropomorphism : ChatGPT doesn't "know," it has no beliefs or intentions, it only generates text. By design, being a generative system, there will always be a margin of error , no matter how much its creators refine the models with updates and reinforcement training.
In professional fields—marketing, consulting, healthcare, finance, data analysis—this has a direct consequence: without human oversight and verification with reliable sources, there is a risk of making decisions based on false data or misinterpretations . The tool can significantly accelerate work, but it can also amplify errors if used without critical filters; to better understand the prospects and risks of combining artificial intelligence, training in governance and controls is essential.
Mistrust, actual use, and limits of knowledge about ChatGPT
In surveys conducted in Spain, a significant number of users admit they don't fully trust ChatGPT's responses . In fact, a considerable percentage cite "lack of confidence in the generated answers" as the main reason for not using the tool more often. And it's not surprising: when you see that it's often right, but occasionally makes serious mistakes, you tend to take it with a grain of salt.
One of the most surprising aspects for people is the temporal limitation of its knowledge base . Classic versions of the model were trained on data up to around 2021, meaning it doesn't have a good grasp of (or is completely unaware of) recent events, legislative changes, technological advancements, or emerging public figures . It can "imagine" a response based on past patterns, but it's not checking in real time what has happened this week.
This deficiency becomes even more evident in specialized fields : very recent law, cutting-edge medicine, highly specific technical niches, or details specific to a country's reality. In these cases, ChatGPT offers incomplete, superficial, or simply incorrect answers , even if they are presented in impeccable writing.
That's why many experts recommend using it as a draft generator or template to work from , never as your sole source. The idea is clear: it saves you time by structuring, summarizing, or giving you ideas, but you should be the one to review, compare, and adjust the content based on your own knowledge and verified external sources.
Typical errors in the way of responding: from the facts to the wording
Beyond hallucinations and a lack of recent knowledge, ChatGPT accumulates a series of common errors in its responses . Some are purely content-related, while others affect the writing style or the understanding of the context. Understanding these errors helps determine when it's appropriate to be suspicious or ask the chatGPT to rephrase.
One of the most frequently cited flaws by experts is its ability to misrepresent verifiable facts . This means it can make mistakes in figures, dates, proper names, or causal relationships that can be easily verified with a simple search in a database or official source. The problem becomes even more acute when it is asked for very specific details that do not commonly appear in the training data.
Incomplete responses are also common , falling short of what you asked for . They might omit important sections, not follow the exact format you requested, or overlook key nuances in your question. Sometimes this happens because the model prioritizes what it interprets as most relevant and leaves out the rest.
Another source of problems is that ChatGPT tries to answer almost everything, even when it doesn't have enough information . Instead of clearly stating "I don't know," it often generates a vague, but somewhat disjointed, response. This is convenient for impatient users, but dangerous when precision and accuracy are needed.
At a linguistic level, the quality in Spanish also shows a decline compared to English . There are more grammatical errors, subject-verb agreement mistakes, punctuation problems, and unnatural turns of phrase. Furthermore, certain typical AI "tics" are repeated—structures like "Not only... but also...", "From... to...", and an overuse of words like "exciting" or "learning"—which immediately reveal that the text was generated by a model.
Problems with context, tone, and natural language comprehension
One particularly problematic aspect is ChatGPT's difficulty in grasping nuances of context, culture, or historical moment . When you ask it questions that require it to be situated within a specific reality—for example, a local political situation, a specific social norm, or a very localized cultural reference—it tends to offer overly general, ambiguous, or irrelevant answers.
This is noticeable, for example, in responses that don't match the tone you requested . Even if you specify that you want something ironic, sarcastic, or very colloquial, the model usually maintains a neutral, polite, and somewhat generic register. And when it tries to reproduce sarcasm or irony, it may do so clumsily, misinterpreting your intention and responding with something completely unrelated to the humorous nuance you intended.
In long conversations, another type of failure appears: loss of context . ChatGPT manages a limited number of tokens (a kind of active memory). When the interaction drags on, some of what was said at the beginning gets lost in that "mental space," and the model starts to forget important details, contradict itself, or repeat explanations it has already given you.
This often leads to contradictions like "you told me A before and now you're saying B," or to answers that seem disconnected from the previous conversation . A good technique to minimize this is to ask them to provide periodic summaries—in just a few points—of what has been agreed upon so far and reuse that summary as context for new questions.
Furthermore, the model may offer rigid or unnatural responses when the user's language is confusing or poorly written . If the prompt mixes many topics in a single sentence, lacks punctuation, or contains serious spelling errors, the likelihood increases that the chatbot will misinterpret the question and respond with something that doesn't quite match what you wanted to know.
Style flaws: texts that “smell” of AI
Those who work daily with digital writing have identified a series of recurring patterns in texts generated by ChatGPT , to the point that they have become clear signs of "this was written by AI." Although they can be easily corrected during a review, it's worthwhile to identify them so your content doesn't sound cloned; there are also tools like the extension to prevent content generated by ChatGPT and other AIs that help detect them.
One of these bad habits is the systematic use of structures like “Not only… but also…” . They are correct, yes, but when they appear repeatedly in the same text, the result becomes tedious and artificial. The same is true for the combination “From X to Y,” which AI frequently uses to list ranges of topics, levels, or benefits.
Another typical trait is the overuse of terms like "exciting," "learning," "discovery," or "enriching experience ." These are valid words, but when they are repeated in almost every paragraph, the text ends up sounding generic, exaggerated, and poorly suited to the actual audience. It's better to replace them with more concrete and descriptive vocabulary, tailored to what you want to communicate.
The AI's influence is also evident in the headlines, where each word begins with a capital letter , a common practice in English but unusual in Spanish. Naturally, Spanish capitalizes only the first word and proper nouns. Correcting this greatly improves the content's appearance, making it seem as if it were written by a human rather than a multilingual model guided by Anglo-Saxon conventions.
Finally, AI often produces texts with a flat, neutral, and impersonal tone . They lack the characteristic filler words, well-placed colloquial expressions, or subtle touches of humor that a human writer would incorporate. Tailoring the style to your brand's voice or your own way of speaking—by adding metaphors, gentle jokes, or relatable references—is essential to prevent the content from seeming like a simple "copy and paste" from a chat.
Common mistakes when using ChatGPT for data analysis and technical tasks
When using ChatGPT as an assistant for Excel, Power BI, SQL, Python, or other data analysis tools , failures depend not only on the model itself but also on how the user interacts with it. Many analysts make planning errors that reduce the quality of the solutions they receive.
One of the most common mistakes is being too generic or vague when formulating the request . Phrases like "Do an analysis for me," "Write an SQL query," or "Help me with Power BI" provide almost no context, so the model can only return basic examples, generic templates, or assumptions that rarely match the actual problem.
Another very common mistake is not providing sample data or explaining the table structure . Without knowing what columns you have, what type of data they contain, or how they relate to each other, ChatGPT can only suggest approximate code. As soon as you try to run it in your environment, you'll start getting errors with field names, incompatible data types, or poorly defined filters.
It's also common not to specify the level of detail or complexity you need . If you're a beginner and just want a step-by-step explanation, the model might return something too technical. And if you're an expert looking for advanced optimization, it might only offer superficial solutions. Clarifying your technical level saves a lot of back and forth.
Finally, many users tend to request "everything at once" in a single, lengthy command : complete dashboards, global reports, KPIs, segmentations, buttons… The result is usually an endless response, difficult to follow and complicated to implement. It's much more efficient to break the problem down into small steps and work on the solution in phases; if you need practical guides for integrating AI with office applications, see how to use AI in Excel and Word.
Inconsistencies, response changes, and service saturation
Another puzzling behavior is that ChatGPT sometimes changes its response to the same question if you repeat it after a while. The general meaning usually remains the same, but details, nuances, or the way it argues may vary. This is due to the probabilistic nature of the model: it doesn't always generate the same text, but rather explores different plausible combinations.
For informal uses this is not usually a serious issue, but in environments where traceability or consistency is required — for example, technical documentation, customer service responses, internal reports — it is important to review, standardize and, if necessary, establish stable templates based on human review of AI outputs.
At the service level, the tool's popularity has also generated a recurring problem: server overload . During peak times, access can be limited, responses take longer, or even occasional errors occur that prevent the conversation from continuing. Although the latest versions of the system attempt to better manage the load, the experience is not always perfect.
Furthermore, spelling, grammar, and punctuation errors have been detected in some contexts , especially when the prompt is ambiguous, poorly written, or mixes several topics in the same sentence. The model does its best to "guess" what you mean, but the result can be strange text with formatting issues that require thorough revision.
All of this reinforces the idea that it's unwise to rely blindly on a single conversation with AI . If the answer doesn't fit, the sensible thing to do is rephrase the question, ask for clarification, or request another version, rather than assuming the first response is the definitive and correct one.
User perception errors: what we (wrongly) expect from ChatGPT
Not all the problems stem from flaws in the model itself: a significant portion of the issues arise from the misguided expectations of those who use it . There's a tendency to project human capabilities onto AI that it doesn't possess, leading to frequent misunderstandings.
A very common mistake is confusing linguistic precision with truth . Because the text is well-written, we assume the content is correct. But we've already seen that the model's priority is constructing coherent sentences, not guaranteeing factual accuracy. This illusion of authority increases the risk of swallowing fabricated data without question.
It's also common to underestimate the impact of how we phrase the question . Changing just a few words in the prompt can completely alter the approach to the response: what information is highlighted, what is omitted, and what interpretations are offered. Believing that "it doesn't matter how I ask, the AI already understands me" is another major self-deception; to improve results, a prompt guide can be extremely helpful.
Many users also tend to interpret humanity where there is only form . When the model apologizes, says "I understand your concern," or uses familiar expressions, it seems to empathize and understand, but in reality, it is only replicating linguistic patterns. This false sense of a person on the other end can lead us to trust it too much.
Finally, it's often assumed that ChatGPT will remember personal rules or details from other sessions , when in reality its memory is limited to the current conversation (except for specific persistent memory functions, which are also limited). Each new chat starts almost from scratch, so you have to explain the context, style guidelines, or restrictions you want it to respect all over again.
Biases, filters, and ethical limits in responses
Another aspect that is often overlooked by users is that ChatGPT is neither neutral nor completely free in its responses . Its outputs are influenced by the data it was trained on—which already contains cultural, gender, geographic, and ideological biases—and by the filters and rules its creators have added to reduce problematic content.
This results in responses that may reproduce stereotypes or human biases , even when attempts are made to mitigate them, and also in messages where the model refuses to answer, redirects the conversation, or responds very generally when it detects sensitive topics (health, politics, misinformation, security, etc.). It is not an “oracle,” but a tool limited by usage policies.
Furthermore, because they don't clearly distinguish between the general rule and the exception, they struggle to understand when an image, piece of data, or example is representative of reality . This has been observed in recurring visual errors in multimodal models: clocks that always show 10:10 because it's the most common time in advertising photos, or difficulties in accurately depicting a person writing with their left hand because images of right-handed people predominate.
These kinds of structural errors show that AI inherits and amplifies the distribution of what it sees online , rather than reasoning from scratch about how the world should be. This is why it doesn't always distinguish well between an anomaly and a rule, which limits its judgment when faced with rare cases.
In corporate contexts, all of this has a clear impact on reputation, regulatory compliance, and risk management . A single biased piece of content or a poorly worded recommendation can quickly escalate on social media, affect brand perception, or even clash with regulations concerning data protection, equality, or truthful advertising.
Best practices for minimizing ChatGPT errors
Given this situation, the solution isn't to stop using the tool, but rather to learn to live with its limitations and minimize its errors . There are several practical guidelines that are very helpful in daily life, both personally and professionally.
The first step is to verify important information with other reliable sources , especially if you're going to publish, make strategic decisions, or address sensitive topics. Comparing data with official documents, recognized studies, or specialized databases remains essential; in professional settings, automated testing for AI models can help detect regressions and errors.
The second step is to improve the quality of your prompts . Being specific, providing context, defining objectives, indicating the level of detail, and specifying the output format (outline, table, list, tone, length, etc.) radically changes the usefulness of the response. The clearer your instruction, the less room there will be for the model to fill in the gaps by making things up.
Third, it's crucial to develop a critical mindset and some knowledge of the domain you're working in . The more you know about the subject, the faster you'll detect inconsistencies, omissions, or biases in the AI's output. You don't need to be an expert in everything, but you shouldn't completely delegate judgment to the machine.
Finally, it's advisable to adapt and humanize the generated texts before using them as is . Adjusting the tone to match your voice or your brand's, removing typical AI filler words, checking spelling and grammar, and reorganizing the structure for better flow will make all the difference between "robotic" content and content that truly connects with your audience.
Mastering ChatGPT today involves understanding both its strengths and weaknesses , leveraging its enormous capacity to generate ideas and save time, while simultaneously establishing clear filters, reviews, and criteria to avoid falling into delusions, biases, or contextual errors. Used wisely, it's a powerful ally; used without control, it can become a silent source of problems and poor decisions.
