The Reliability of Artificial Intelligence: Errors, Context, and a Lesson From the Past
A few days ago, during a conversation with some friends from my hometown, someone made a comment I’ve heard more than once in recent months: “You can’t trust artificial intelligence, because AI makes mistakes.” It wasn’t an aggressive or malicious statement. It was said with caution — almost like a warning. Someone else had told me something similar not long before.
What’s interesting is that while I listened to that argument, I kept thinking about my daily work. I use AI regularly: to code, to write, to organize ideas, to solve real problems. And my experience didn’t fully match that blunt conclusion. Not because AI is perfect — it isn’t — but because when used correctly, it’s often surprisingly reliable.
That’s where the real question behind this article comes from: is the problem really the reliability of AI, or the way we’re using it?
“AI Makes Mistakes”: A Truth That Doesn’t Go Far Enough
Let’s start with the obvious. Yes, artificial intelligence makes mistakes. No serious person denies it. Sometimes it gets facts wrong, sometimes it misinterprets a question, sometimes it fills gaps with answers that sound convincing… but aren’t correct. That phenomenon has become widely known as hallucinations.
But stopping there means staying on the surface. Because if we apply that criterion strictly, we would have to distrust almost every tool created by humans.
Humans make mistakes.
Books contain errors.
Academic papers get corrected over time.
Teachers — even great ones — can say something wrong in class.
The internet is full of false or incomplete information.
And still, we don’t discard books, education, writing, or human knowledge in general. What we do — or should do — is learn to use those tools with judgment.
An error doesn’t invalidate a tool. What invalidates it is using it uncritically.
The Factor That Is Almost Always Ignored: Context
In my experience, one of the biggest misunderstandings about AI reliability has to do with context — or more precisely, the lack of it.
When an AI receives vague, incomplete, or ambiguous instructions, it does the only thing it can do: it tries to generate a plausible answer. Not because it “wants to deceive,” but because its function is to continue language patterns in the most coherent way possible based on what it has learned.
On the other hand, when AI works with strong context, results are usually very different.
In programming, for example, it’s not the same to ask:
“This code doesn’t work, what do I do?”
as it is to explain:
“I’m working in X language, in this environment, with this version, here is the exact error, here is the goal, and here’s what I’ve already tried.”
In the second case, the quality of the answer changes dramatically. Not because AI is magical, but because context reduces the need for assumptions.
This leads to a first key conclusion:
👉 AI reliability isn’t absolute — it’s contextual.
Artificial Intelligence Doesn’t Come From Nothing
Another point that is rarely explained clearly is this: AI doesn’t appear in a vacuum. It doesn’t create knowledge from scratch. It learns from existing human text.
Books, articles, technical documentation, forums, debates, manuals, Q&As, public discussions… all of that becomes part of the material language models are trained on.
This has a direct and logical consequence:
if human knowledge contains errors, contradictions, or gaps, AI can reflect them.
Many AI mistakes are not new mistakes. They are inherited mistakes. Poorly explained ideas, concepts repeated thousands of times even if they’re wrong, outdated information that keeps circulating. AI doesn’t “know” something is false — it knows something is common.
In other words:
AI doesn’t only amplify the best of human knowledge — it also amplifies its imperfections.
And far from being shocking, that is deeply human.
This behavior doesn’t happen because of “malice” or an intention to deceive, but because of how language models work. AI is designed to produce a likely and coherent response, even when it lacks enough information or when the question is ambiguous. In fact, recent research explains that models may generate plausible but incorrect answers (so-called hallucinations) because standard training and evaluation methods tend to reward confident answers instead of rewarding the ability to recognize uncertainty (Kalai et al., 2025).
A Lesson From History: Ancient Scribes
To understand this better, it helps to look back — much further back.
For centuries, knowledge was transmitted by hand. Literally. Scribes — trained professionals — copied texts word by word. They were educated people dedicated to that craft, often among the few who could read and write in their time. Their work was slow, manual, and demanding.
And still, they made mistakes.
Not because they were incompetent. Not because they didn’t care about the text. But because they were human.
They confused similar letters.
They skipped lines without noticing.
They repeated words.
They omitted fragments.
They introduced small “clarifications” that sometimes changed the meaning.
The result is that almost no ancient manuscript exists without copying errors.
The New Testament and Textual Variants
A particularly well-known example is the New Testament. Today, we have thousands of manuscripts in Greek, Latin, and other ancient languages. When scholars compare them, they find hundreds of thousands of textual variants.
This fact is often presented sensationally, as if it were proof that the text is “unreliable.” But experts in textual criticism interpret it in a very different way.
The vast majority of variants are minor: spelling differences, word order changes, small omissions. They are, essentially, normal human copying errors accumulated over generations.
And yet, by comparing manuscripts, specialists can reconstruct the text with a very high level of confidence. The existence of errors doesn’t invalidate the content — on the contrary, it shows how the transmission of human knowledge works.
This is where the analogy with AI becomes clear.
Just as scribes inherited errors from previous manuscripts and added some of their own, AI inherits errors from digital human knowledge and, in certain contexts, may reproduce them.
Let’s Return to the Right Question
At this point, the question “Does AI make mistakes?” becomes almost irrelevant. The answer is obvious: yes.
The interesting question is different:
Under what conditions is AI reliable?
And the answer — based on practical experience and history — is quite clear:
- AI is more reliable when it has strong context
- AI is more reliable when used as support, not as absolute authority
- AI is more reliable when humans maintain judgment and verification
- AI is less reliable when asked for certainty where there is only ambiguity
AI reliability isn’t binary. It’s not “reliable” or “unreliable.” It’s gradual, situational, dependent on usage.
AI as a Tool, Not a Replacement for Judgment
This may be the most important point in the entire article. AI works best when it is understood for what it is: an extraordinarily powerful tool — but not the final judge of truth.
Using it well means:
- providing context
- asking better questions
- requesting clarification when needed
- validating critical information
- accepting that final judgment remains human
This isn’t a weakness of AI. It’s historical continuity. Human knowledge has always advanced this way: with errors, corrections, revisions, and improvements.
Conclusion: Error Is Not the Enemy
The history of knowledge is not the history of perfection — it’s the history of constant correction. Scribes made mistakes. Authors make mistakes. Teachers make mistakes. And AI does too.
The difference isn’t the existence of error — it’s how we handle it.
Used correctly, with context and judgment, AI is one of the most reliable and productive tools we’ve ever had. Used poorly, without critical thinking, it simply amplifies confusion.
That’s why the reliability of artificial intelligence doesn’t depend only on the machine.
It depends — as it always has — on the human being using it.