The magic ingredient in a Transformer is self-attention. Instead of processing words one by one, the self-attention mechanism allows the model to look at all the other words in the input sentence simultaneously and weigh their importance relative to each other.

The architectural pattern of Retrieval-Augmented Generation (RAG) has proven to be a transformative solution for grounding Large Language Models (LLMs) in external, verifiable knowledge.

At its core, a scaling law in AI is a predictable relationship showing that as you increase the resources for training a model, its performance gets better in a smooth, measurable way.

The recent surge in large language model (LLM) applications has brought both incredible potential and significant challenges, namely their propensity for generating inaccurate or outdated information.