LLM Architecture: Transformer, Attention, Parameters

Architecture and Working Principle

To understand how modern AI "thinks," we need to look at its structural components.

Transformer

The architecture that forms the foundation of modern LLMs (GPT, Claude, Llama, etc.). It solves the relationships between words using the "attention" mechanism, allowing for parallel processing of data.

Attention Mechanism

The structure that allows the model to decide which words are most related to each other while processing a sentence. It enables the model to understand context and nuance (e.g., understanding "bank" based on "river" or "money").

Parameters

The "units of knowledge" a model learns during training. A model's parameter count (e.g., 70B - 70 Billion) is generally an indicator of its complexity and capacity to handle intricate tasks.

Context Window

The maximum amount of data (measured in tokens) that a model can "keep in mind" at one time. A larger context window allows the model to process longer documents or conversation histories.

Relevance to Data Analysis

Understanding context windows is crucial when designing Cloud & IoT Solutions that utilize AI. If you want to analyze a month's worth of log data, the model must have a context window large enough to ingest that data, or the data must be summarized first.