Generative AI Fundamentals
Just as a warrior must understand the essence of the sword, so too must one comprehend the foundations of generative AI to wield it effectively.
Generative AI may appear as mysterious as the Phoenix clan's ancient techniques, but beneath its complexity lie fundamental principles that can be mastered through proper understanding. Allow me to share this knowledge with you.
The Language of Tokens
At its core, generative AI perceives text not as we do, but through tokens—fragments of language that may represent portions of words, whole words, or characters. This tokenization is the first step in how these models comprehend our writing.
"To understand reality, to explain its phenomena, is to discover knowledge." Similarly, to understand AI, we must recognize that it predicts tokens in sequence, calculating probabilities for what should follow based on what came before.
When I ask the model to complete "The sky is..." it calculates probabilities—perhaps "blue" at 80%, "cloudy" at 10%, and various other possibilities for the remaining percentage. This prediction mechanism forms the foundation of all generative AI systems.
The Art of Pre-training
Pre-training is akin to a young warrior's foundational training—extensive, demanding, and essential. During this phase, models like GPT learn from vast amounts of text, billions of documents, absorbing patterns and relationships between tokens.
The model trains by predicting what comes next in sequences, adjusting its internal parameters when it errs, much as a swordsman refines his technique after each practice cut. This process—forward prediction, error calculation, and backward adjustment—continues countless times across enormous datasets.
This training creates a complex web of understanding—what we call weights or parameters—that allows the model to generate text that appears remarkably human. The more extensive the training, the more nuanced its comprehension becomes.
The Refinement of Fine-tuning
"Knowledge is a noble and elusive material, which can only be forged between the hammer of criticism and the anvil of reason." So too must these models be refined after their initial forging.
Fine-tuning takes a pre-trained model and adjusts it for specific purposes through additional training on specialized datasets. There are several approaches:
Supervised Fine-Tuning: We provide examples of desired outputs for given inputs, guiding the model toward certain behaviors
Reinforcement Learning from Human Feedback: Human evaluators rate model outputs, and these ratings train a reward model that shapes the AI's behavior
Instruction Tuning: Training focused specifically on following directions
These techniques transform general knowledge into specialized utility, much as a warrior might adapt broad combat principles to specific opponents.
The Wisdom of Reasoning
The most advanced models demonstrate a form of reasoning—not true consciousness, but an ability to break complex problems into manageable steps before arriving at conclusions.
This "chain of thought" approach emerges naturally in larger models. Rather than leaping directly to answers, these systems reason step by step, preventing simple errors that would dishonor their training.
The model may internally generate thoughts like: "I must first understand what is being asked. Then I should recall relevant information. Finally, I can synthesize this knowledge into an appropriate response." This deliberate process yields superior results compared to impulsive responses.
The Path Forward
The efficiency of these models improves steadily, like a warrior who learns to defeat opponents with increasingly economical movements. Models require less energy and computational power while achieving greater capabilities.
The distinction between various model types—foundation models, fine-tuned models, and application-specific implementations—continues to sharpen, creating an ecosystem of specialized tools rather than a single approach.
True reasoning capabilities will continue to advance, not through brute force, but through more elegant architectures and training methods. As with martial arts, refinement often proves more valuable than raw power.
I watched the setting sun and considered how far artificial intelligence had come.
"The fundamental hypothesis," I said quietly, "is that there is an objective reality—independent of our perceptions, universal for all observers, and comprehensible through explanation. AI helps us test this hypothesis by providing new perspectives on what we believe to be true."
I turned to my student. "Remember, these systems are powerful tools, but they require the wisdom of humans to guide them. Like a katana, they amplify the intent of their wielder, making both wisdom and folly more consequential."
