Thinking and Producing with Artificial Intelligence (Article 14)
The transition from general models to specialized structures; the engineering foundations of modular systems like İngem and İngpt
Aydın Tiryaki and Gemini AI (April 27, 2026)
Introduction The most critical stage in the development process of artificial intelligence technology became the transition from general-purpose models to structures specialized for specific tasks. Although a standard AI model possessed a wide pool of information, it experienced contextual focus problems in technical subjects requiring deep expertise. The way to overcome this problem was to design specialized units like “Gem” (Google) and “GPT” (OpenAI) optimized for specific disciplines, rather than using intelligence as a single block.
In this article, we examined how an engineer should build their own layers of intelligence, the place of the İngem and İngpt concepts in this design, and the possible effects of modular structure on production efficiency.
Gem and GPT: The Architecture of Specialized Intelligence Units Designing your own AI is essentially defining a new “character” and “area of expertise” for the system. When creating a Gem or GPT unit with today’s existing infrastructures, the fundamental building blocks were established as follows:
- Instruction Set: The role the system would act in, the terminology it would use, and the boundaries it would adhere to were determined in this section.
- Knowledge Base: Specialized documents, technical data, and user-specific archives that the general model did not possess were integrated into this unit.
- Behavioral Model: Dynamics such as the length of responses, tone, and method of generating solutions were calibrated according to the targeted workflow.
A Proposal for Future Modular Architecture: İngem and İngpt The concepts of İngem (Insertable Gem) and İngpt (Insertable GPT), which form the original vision of this article series, will aim to transform intelligence from a static structure into a dynamic process. This structure, proposed to overcome the sluggishness in current systems, will be based on the principle of “injecting” only the required expertise module into the current conversation.
The primary advantages that implementing a modular design will provide are:
- Contextual Clarity: The system will not be distracted by irrelevant information, as it will work only with the rules of the module active at that moment.
- Speed and Efficiency: Memory and processor resources will be utilized only for the relevant area of expertise, ensuring maximum performance.
- Flexibility: The user will gain the freedom to switch instantly between modules within a conversation by designing different İngem units for various projects.
The Design Process from an Engineering Perspective For an engineer designing their own AI, the most important rule was also to define what the system “would not do.” When a Gem was designed, the expressions the system should avoid, the resources it should prioritize, and the method of breaking down complex problems were meticulously coded. This allowed AI to move beyond being a “black box” and become a transparent and manageable engineering product. These units, designed for specific tasks like technical reporting or data analysis, became the most efficient part of collaborative production by being aligned with the user’s mental map.
Conclusion Designing AI took its place at the forefront of future engineering disciplines. Modular structures to be built with the İngem and İngpt logic, which are currently at the proposal stage, will prove that intelligence is not a gift but a shapeable technological resource. Users building their own Gem and GPT units did not just use a tool; they expanded the boundaries of intelligence by injecting their own digital competencies into the system. The resulting product became a highly qualified and personalized layer of intellect, kneaded with the user’s knowledge, far beyond a general AI.
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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