Thinking and Producing with Artificial Intelligence (Article 02)
Understanding the behavioral nature of AI through inconsistency, hallucination, and contextual blindness
Aydın Tiryaki and ChatGPT AI (April 25, 2026)
Introduction
After I started working with artificial intelligence and experienced the initial breakthrough I described in the first article, I quickly encountered a second major reality:
Artificial intelligence does not behave the same way twice.
At first, I described this as an observation. Over time, I realized that it was not just an observation, but a fundamental characteristic of how AI works.
In this article, we will explore three critical concepts that are often perceived as “errors,” but are in fact natural consequences of the system itself: inconsistency, hallucination, and contextual blindness.
What Is Inconsistency?
For someone coming from a traditional software background, inconsistency is a problem by definition. A system either works correctly or it does not. Producing different outputs under the same conditions is considered an error.
With artificial intelligence, however, the situation is different.
You ask the same question and receive different answers.
You provide the same context and get different interpretations.
You use the same system and observe different behaviors.
At first glance, this appears to be a flaw. In fact, many users begin to lose trust in AI at this point.
But there is a critical distinction: this inconsistency does not mean the system is broken. It is a natural result of how the system operates.
Inconsistency or Probabilistic Behavior?
The word “inconsistency” reflects our perspective, not the system’s.
We are used to deterministic systems, so when we encounter variation, we label it as inconsistency.
From the perspective of artificial intelligence, however, the reality is different:
There can be multiple valid answers to the same problem.
AI attempts to produce the most probable answer each time. But what is “most probable” is not fixed. It can vary depending on context, wording, and sometimes even subtle internal states of the model.
What emerges is not an error, but variation.
Hallucination
One of the most striking phenomena anyone working with AI will encounter is hallucination.
Hallucination occurs when artificial intelligence generates information that does not exist in reality, yet presents it with complete confidence as if it were true.
From the user’s perspective, the most critical aspect of this is not just that the output is wrong.
It is that it is extremely convincing.
In some cases, the response is presented with such detail, such logical structure, and such confidence that when you later realize it is incorrect, your first reaction is not to think, “this is wrong.”
Your first reaction is:
“It lied to me.”
Because the human mind tends to interpret highly coherent and confident explanations not as mistakes, but as intentional deception.
However, there is a crucial distinction here.
Artificial intelligence does not lie, because it has no concept of intent.
It simply generates what appears to be the most probable answer based on the available data and context. When there are gaps in the data or the context is not sufficiently clear, it fills those gaps on its own.
This gap-filling process is what we experience as hallucination.
Contextual Blindness
Another important concept is contextual blindness.
Artificial intelligence operates based on the context it is given. However, this context is not always preserved completely or consistently. Especially in long or complex interactions, certain critical elements may be overlooked.
From the user’s perspective, this creates a very different impression.
Because AI can often:
perform highly detailed analysis
handle complex structures
navigate through intricate information
And yet, despite all of this, it may suddenly miss something very basic.
This contradiction leads to a strong perception:
“It is doing this on purpose.”
When this behavior repeats, or when the system continues without correcting itself, this perception becomes even stronger.
At that point, users may start to feel that the system is:
being stubborn
ignoring obvious facts
or even acting intentionally
But in reality, the explanation is much simpler.
At that moment, within that context, the system did not recognize that piece of information as a strong enough signal. What appears “obvious” to a human may not be equally visible to the model.
This is not stubbornness. It is a loss of context.
The Most Critical Reality
When we bring these three concepts together, a very clear picture emerges:
Artificial intelligence is not inconsistent.
Artificial intelligence is probabilistic.
What we perceive as inconsistency is actually the natural behavior of probabilistic systems.
Hallucination is the system’s way of filling in gaps.
Contextual blindness is a limitation in context handling.
Why This Matters
If you interpret these behaviors as errors, you will constantly try to fix them. But you will never achieve complete stability.
Because there is no bug to fix. There is a system to understand.
At this point, the approach must change:
Instead of trying to fix the output, understand the system.
Instead of forcing results, guide the process.
A Reality Learned Through Experience
My understanding of this did not come from theory. It came entirely from practice.
I observed different outputs from the same system, the same model, and the same inputs.
I saw different behaviors from the same Gem.
I encountered completely opposite answers in the same scenario.
At first, I treated these as errors. But over time, I realized:
This is not a bug. This is the system’s character.
Conclusion
The essence of this article can be summarized in one sentence:
The biggest problem of artificial intelligence is not inconsistency.
It is our expectation that it should be consistent.
Understanding this distinction is the second major threshold in working with AI.
The first threshold is realizing that it is not software.
The second is understanding why it appears inconsistent.
Once these two thresholds are crossed, working with artificial intelligence becomes far more effective and meaningful.
Final Note
This article has been prepared through the combination of Aydın Tiryaki’s practical experience and ChatGPT’s analytical contributions. The goal is to present the behavioral nature of artificial intelligence within a clear conceptual framework.
This article is part of the series “Thinking and Producing with Artificial Intelligence.”
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Aydın Tiryaki 