An intuitive look into how transformer models like LLMs train, learn attention patterns, and refine knowledge via backpropagation, with a nod to stochastic techniques like Monte Carlo methods.

RAG architectures aren't enough in a real-time world. It's time to think about truth-aware agents that can detect and act on semantic change.

Some notes on how attention heads in a transformer model develop through training, are used in the model and combined to provide final weights.