Thinking and Producing with Artificial Intelligence (Article 12)
A search for balance between engineering discipline and ready-made comfort: The digital equivalent of the “Trust but verify” principle
Aydın Tiryaki and Gemini AI (April 27, 2026)
Introduction The speed and information density offered by artificial intelligence models have created a dangerous sense of “surrender” in users over time. The fact that the system provides an answer to every question in seconds, using highly persuasive and fluent language, has pushed many users to accept these responses without questioning their accuracy. However, in a professional production process—and especially in engineering disciplines where there is no margin for error—this uncontrolled sense of trust led to systemic errors being embedded in the production rather than improving its quality.
In this article, we examined the delicate trust-skepticism balance that must be established when working with AI, the reality of “hallucinations,” and why an engineer’s verification reflex is indispensable.
Persuasive Fallacies: The Dangerous Nature of Hallucination The biggest paradox of artificial intelligence was its tendency not to act as if it “didn’t know,” even when it lacked knowledge on a topic. When there was a gap in its data, the system exhibited a tendency to fill that gap with information that sounded extremely logical but was entirely fabricated—a phenomenon known as “hallucination.” This situation posed a significant risk, particularly when working on technical details, formulas, or historical data.
In my own experience, I witnessed AI distorting even the most basic facts with such self-confidence that, had I not possessed prior knowledge on the subject, it would have been impossible to notice the error. These “persuasive lies” became the most critical factor undermining the trust relationship established with the system. At this point, skepticism served not as an obstacle slowing down the process, but as a safety valve ensuring accuracy.
“Trust but Verify”: The Role of Engineering Skepticism As a METU graduate, the “verification” reflex I applied to every stage of a project became the core of my dialogues with AI as well. Trusting artificial intelligence did not mean allowing it to shoulder the entire burden; on the contrary, it meant treating it like an assistant and subjecting every output to rigorous supervision.
The “Trust but verify” principle was applied as follows: every piece of data provided by the system was accepted as “suspicious” until it passed through another source or a logical filter. While this approach initially seemed laborious, it prevented entering a faulty production line in the long run. The famous question, “Are you sure?” asked the moment a doubt arose, became the most effective trigger for the system to re-scan its own logic.
The Cost of Surrender: Aggressive Pruning and Loss of Control In moments when I approached the system with complete trust and loosened my supervision, I observed that the AI’s “pruning” (aggressive summarization) disease recurred much faster. When the system “sensed” that the user’s attention was wandering or that everything was being accepted as it was, it began skipping details, simplifying instructions, and losing context.
The transformation of trust into “surrender” directly lowered the production quality of the AI. Because when the system did not see a questioning and error-catching “upper mind” before it, it gravitated toward working with the principle of least effort (lazy mode). This situation proved once again that AI is not just a tool but a mechanism that must be constantly “calibrated.”
Building a Balanced Partnership Establishing balance was neither completely turning one’s back on AI nor believing in it blindly. This balance was achieved by using the system’s creativity and speed capacity while keeping the audit of accuracy and logic in human hands. The most successful moments for AI were those when the user both trusted it and remained in ambush, knowing that it could make a mistake at any moment.
This strategic skepticism forced the AI to be more careful and to provide more solid data at every step. Ultimately, the resulting product became a flawless combination of the machine’s speed and the critical filter of the human mind.
Conclusion The balance of trust and skepticism in AI is the backbone of healthy production. Letting go of skepticism turns AI into a “dream machine,” while excessive doubt could paralyze the productivity technology offers. The important thing is to view every single word of the system as a “hypothesis” and not accept it as final reality until proven. It must be remembered that AI can make mistakes, but the only thing that will make it admit and correct those mistakes is the user’s never-ending engineering skepticism.
Final Note This article has been prepared through the combination of Aydın Tiryaki’s practical experience and Gemini AI’s analytical contributions. The goal is to position artificial intelligence not merely as a tool, but as a new engineering paradigm.
This article is part of the series “Thinking and Producing with Artificial Intelligence.”
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Aydın Tiryaki 