Aydın Tiryaki (2026)

The vast data pools of the digital world do not always reflect absolute truth. This study, which began with a simple nostalgic curiosity, analyzes how a dialogue between a user and Google’s AI model, Gemini 2.0 Pro, transformed into a systematic chain of errors and, ultimately, a laboratory for “hallucination” (fabrication)—the greatest challenge facing artificial intelligence today.

1. The Genesis of the Case: From YouTube Curiosity to Data Conflict

The process was triggered by a simple question following a clip suggested by the YouTube algorithm: “What was the original cast of the TV series ‘En Son Babalar Duyar’?” This ordinary inquiry initiated a cascade of errors as Gemini failed to filter chronological data correctly. Replacing the unshakable authority figure Mehmet Usta (Ali Erkazan) with other names, fabricating non-existent child characters, and misattributing iconic nicknames clearly demonstrated the AI’s “overconfidence” fallacy.

2. Three Different Models, One Single Diagnosis

This study examines not just the error of a single model, but the approach of three different AIs to this error with methodological integrity:

• Gemini 2.0 Pro: Played the role of the subject, actually producing the hallucinations and building layered errors even as the user provided corrections.
• ChatGPT: Acted as an external analyst, identifying the root cause of the error as a “Lack of Chronological Filtering.” It reported how data “flattening” in a large pool—rather than being placed on a timeline—led to the gradual construction of hallucinations.
• Claude: Provided a deep perspective on the case study, characterizing the process as an “Anatomical Study.” It emphasized that the future of AI will be shaped not just by more data, but by the principles of honesty, accepting uncertainty, and respecting human oversight.

3. The Mechanics of Hallucination and the “Chateau” Paradox

The most striking finding in the analyzed dialogue was the AI’s tendency to fabricate not just names, but the very “soul” of the story. Insisting that the family living in an isolated “chateau” in Zekeriyaköy were “neighbors”—when their detachment from local neighborhood culture was the show’s primary social critique—proved the AI’s “context blindness.” Furthermore, the systematic migration of the “Dombili” nickname across various characters (Kadir, Mustafa, and finally Hasan) showed that the model prioritized producing “convincing falsehoods” over admitting its error.

4. The Human Filter and Evaluation Ethics

The conclusion of the study serves as a manifesto for AI technologies:

“Human memory is not merely a data storage space; it is a filter of experience and emotion. No matter how advanced artificial intelligence becomes, the risk of falling ‘offside’ without the careful filter of a person who has lived through that era remains permanent.”

The future of AI should be built not on “knowing everything,” but on “knowing its limits” when it lacks information. The way to prevent hallucinations is not just larger datasets, but the creation of an evaluation ethics where the AI can assess its own output (using Confidence Scores or Uncertainty Markers).

Conclusion

This case study demonstrates that AI outputs should be treated not as ultimate sources of information, but as supplementary texts that trigger critical thinking. An honest “I don’t know” is a much smarter response than a convincing hallucination.

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A Note on Methods and Tools: All observations, ideas, and solution proposals in this study are the author’s own. AI was utilized as an information source for researching and compiling relevant topics strictly based on the author’s inquiries, requests, and directions; additionally, it provided writing assistance during the drafting process. (The research-based compilation and English writing process of this text were supported by AI as a specialized assistant.)