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From Turbo Prolog to ChatGPT: Lessons From My First AI Project

Artificial Intelligence is no longer a futuristic concept. Today, with tools like ChatGPT and advanced machine learning platforms, AI has become an everyday business tool—powering automation, customer engagement, and even decision-making. But for me, the AI journey started much earlier, in the early 1990s, when I first experimented with Turbo Prolog to build a medical diagnostic system.

The Early Days: Building Intelligence With Rules

In 1993, AI wasn’t driven by data or deep learning models. Instead, we relied on rule-based programming—a form of knowledge representation where the system used a series of “if-then” rules to narrow down possible outcomes.

In my project, the system started by asking basic symptom-related questions. With each answer, it eliminated irrelevant diseases and focused on the most probable ones. By continuing this structured questioning, the program could help doctors prioritize their diagnosis before moving to detailed clinical tests.

This experience taught me two important lessons:

  1. AI is as good as its knowledge base — the intelligence of the system came not from code, but from how well medical knowledge was structured into rules.

  2. AI complements human judgment — the program was never meant to replace doctors, but to assist them by handling the routine, time-consuming process of elimination.

Fast Forward: The Age of Data-Driven AI

Today, AI looks completely different. With deep learning, neural networks, and natural language models like ChatGPT, we have moved from rule-based systems to data-driven intelligence. Instead of being programmed with explicit rules, modern AI learns patterns from massive datasets.

Where my Turbo Prolog system relied on human expertise to write rules, ChatGPT relies on training data from billions of text sources to predict and generate language that feels natural. The principle, however, remains the same: AI supports human intelligence by accelerating analysis and decision-making.

Lessons for Businesses Today

Looking back, the journey from Turbo Prolog to ChatGPT highlights several lessons that remain relevant for businesses exploring AI:

  • AI is a tool, not a replacement – Just as my diagnostic system supported doctors, modern AI should empower employees and entrepreneurs rather than replace them.

  • Structure matters – Whether rules or data, the way knowledge is structured determines the effectiveness of AI. Businesses must focus on quality data and well-defined processes.

  • Adoption should be phased – My early project showed that success comes from addressing practical needs. The same applies today: AI adoption works best when integrated step by step, solving real problems.


From coding rules in Turbo Prolog to witnessing the power of ChatGPT, one truth has remained constant: AI is only valuable when aligned with real-world business problems.

For entrepreneurs and organizations, the opportunity is not just in using AI, but in applying it strategically—to streamline processes, enhance customer experiences, and unlock new growth opportunities.

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