Jay Alammar on LLMs, RAG, and AI Engineering

AI Transformations

The journey of AI models has been marked by significant transformations, leading to the rise of transformers. Jay Alammar reflects on the early days when deep learning models began to revolutionize software capabilities. He highlights the pivotal moment when language models emerged, transforming the landscape with their scale and capabilities 1. Jay emphasizes the importance of understanding these models' training and limitations, as misconceptions can lead to unrealistic expectations 2.

We should raise awareness of how to think about these models, how they were trained and what they are good at, and what they're nothing.

--- Jay Alammar

This awareness is crucial for leveraging AI's full potential in real-world applications, particularly in semantic search and classification.

   

Model Evolution

The evolution of transformer models has been a fascinating journey of technological advancements and architectural updates. Jay Alammar discusses the transition from encoder-decoder models to text generation models, which now dominate the field 3. He notes that while the core architecture has remained relatively stable, innovations like positional encoding and attention layer efficiency have significantly enhanced model performance.

There are a lot of different improvements suggested on the architecture in the previous years, but a few other things that really stood the test of time are things like positional encoding.

--- Jay Alammar

Jay also mentions the updated version of the Illustrated Transformer, reflecting these advancements and providing insights into the current state of AI architectures 4.