An LLM's core training objective—predicting the next token—makes it sensitive to the raw frequency of words and numbers online. This creates a subtle but profound flaw: it's more likely to output '30' than '29' in a counting task, not because of logic, but because '30' is statistically more common in its training data.

An LLM's core function is predicting the next word. Therefore, when it encounters information that defies its prediction, it flags it as surprising. This mechanism gives it an innate ability to identify "interesting" or novel concepts within a body of text.

The key difference between modern AI and older tech like Google Search is its ability to reason about hypotheticals. It doesn't just retrieve existing information; it synthesizes knowledge to "think for itself" and generate entirely new content.

To explain the LLM 'temperature' parameter, imagine a claw machine. A low temperature (zero) is a sharp, icy peak where the claw deterministically grabs the top token. A high temperature melts the peak, allowing the claw to grab more creative, varied tokens from a wider, flatter area.

Unlike chatbots that rely solely on their training data, Google's AI acts as a live researcher. For a single user query, the model executes a 'query fanout'—running multiple, targeted background searches to gather, synthesize, and cite fresh information from across the web in real-time.

Contrary to popular belief, generative AI like LLMs may not get significantly more accurate. As statistical engines that predict the next most likely word, they lack true reasoning or an understanding of "accuracy." This fundamental limitation means they will always be prone to making unfixable mistakes.

Setting an LLM's temperature to zero should make its output deterministic, but it doesn't in practice. This is because floating-point number additions, when parallelized across GPUs, are non-associative. The order in which batched operations complete creates tiny variations, preventing true determinism.

AI tailors recommendations to individual user history and inferred intent, such as being budget-minded versus quality-focused. This means there is no single, universal ranking; visibility depends on aligning with specific user profiles, not a monolithic algorithm.

LLMs are trained to produce high-probability, common information, making it hard to surface rare knowledge. The solution is to programmatically create prompts that combine unlikely concepts. This forces the model into an improbable state, compelling it to search the long tail of its knowledge base rather than relying on common associations.