Overview of current OpenAI services and products

A deep dive into how transformers separate syntactic structure from semantic meaning using attention heads, layer depth, and learned latent spaces.

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.