What AI Models Really Learn From Websites
Introduction When people think about how AI models interact with websites, they often imagine a simple process where the model reads the […]
Category: LLM Behavior & Interpretation
This category explores how large language models perceive, interpret, and reconstruct meaning from digital content. It focuses on the internal mechanics of AI comprehension, including tokenization, contextual reasoning, pattern recognition, and the probabilistic nature of AI understanding. Articles in this category break down the invisible processes that shape how AI systems read the web, answer questions, and generate insights.
Readers gain a deeper appreciation for the strengths and limitations of LLMs, especially when dealing with ambiguous, inconsistent, or poorly structured content. This category helps bridge the gap between human intuition and machine interpretation, showing why AI often struggles with tasks that seem trivial to humans and why structured metadata is becoming essential for accurate comprehension.
How LLMs Actually Process Web Content
Introduction Large Language Models (LLMs) have become central to how people interact with information online. They summarize articles, answer questions, generate insights, […]