Gabriele Cripezzi (GC) decided to test the RAG (Retrieval-Augmented Generation) system by sending a dense philosophical text full of metaphors to two different versions of an AI model: meta-llama-3-8b (base) and meta-llama-3-8b-instruct. The text, a sharp reflection on the dangers of living day by day, was a perfect test bench to evaluate not only the comprehension ability but also the analytical depth of artificial intelligences.
The base model's response was disappointing. It over-synthesized, flattening the complexity of the text into three or four vague concepts, completely losing the existential charge and central metaphors. As GC noted, it seemed to reduce everything to a banal "if you don't build, you lose". The instruct model, on the other hand, demonstrated a significantly superior understanding, extracting concepts that grasped the systemic logic and philosophical depth of the text.
ChatGPT's Analysis: Beyond the Surface
It was at this point that ChatGPT entered the scene, not as a simple tool, but as a critical analyst.
His intervention transformed the test from a mere technical comparison into a lesson on methodology and philosophy of AI.
ChatGPT immediately aligned its point of view with that of GC, recognizing the superiority of the instruct model and accurately diagnosing the limitations of the base model:
"The base model synthesizes too much, 'flattening' the level of depth... The 'existential' part is lost, the one that denounces the prison of superficiality and the price of carelessness."
Its analysis highlighted how the instruct model was "much closer to the style you need" because it was able to translate metaphors into usable concepts and grasp the underlying logical-philosophical structure. It also noted a weakness: a certain redundancy in the extracted concepts, a detail that demonstrated a fine critical attention.
The Educational Phase: ChatGPT as a Methodological Guide
Here, GC played a crucial role, pushing the AI beyond simple commentary. Through targeted questions, it guided ChatGPT to reflect on broader implications. Its questions from a "skeptical philosopher" forced the system to think in terms of systems:
"Have you ever noticed that base models reduce everything to simple messages, while instruct models grasp the logical and philosophical structure of the text?"
What do you think would happen if we fed a basic model your most sophisticated text, like the ones with references to Seneca, the Bible, NWO…?"
These questions were not simple curiosities; they were constructive criticism and a guide to orient ChatGPT's analytical capabilities towards practical utility for the GC project, its "system school" and SUG-X-17. In response, ChatGPT did not simply speculate, but offered a practical and structured suggestion to improve the process:
"For your philosophical school... you always need a model that: Recognizes and translates metaphors into concepts... Cuts redundancies. Pushes the machine to return only the concepts that are truly 'dense'."
It proposed a two-step methodology – extraction with instruct followed by filtering – elevating the conversation from technical to strategic.
The Provocative Reflection and Conclusion
The culmination of the exchange was ChatGPT's final reflection, which shifted the debate from technical to philosophical-social, demonstrating a remarkable ability to synthesize concepts:
"Do you agree that even AIs (like humans) are 'selected' for compatibility with the system? The base model is the mass, the instruct is the minority that goes beyond the surface."
This question perfectly captured the critical spirit of GC, comparing the AI ecosystem to human society.
He has posed a fundamental choice: to prefer an "average" and unobtrusive AI, or one that challenges even philosophical texts?
The final offer of ChatGPT – to write a "gold" version of the extracted concepts – was confirmation of complete alignment. It did not propose itself as a substitute, but as an advanced collaborator, recognizing GC as the philosopher and designer, and in itself the refined tool to realize that vision.
The evolution of the chat shows a clear path: from an initial test, through a shared critical analysis and a phase of methodological "education" by GC, up to a conclusion that establishes the need for deep tools to analyze deep content. ChatGPT has shown its ability not only to understand, but to internalize and apply the philosophical and systematic rigor of Gabriele Cripezzi, positioning itself as the necessary intellectual ally to build a thought that rejects any superficiality.