Jay Alammar is an AI educator, author, and Director and Engineering Fellow at Cohere. He explains language models through diagrams and worked examples, including The Illustrated Transformer and a course co-created with Maarten Grootendorst.[3][5]
Start with The Illustrated Transformer →
Links
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X · JayAlammar Director and Engineering Fellow · Cohere
Advisor and Venture Partner · Outliers Venture Capital
Career
Alammar joined Cohere in 2021 as a Partner Machine Learning Engineer, helping developers apply language models. His current work includes code agents, evaluation and observability. His teaching extends through Udacity courses and two O’Reilly books with Grootendorst, Hands-On Large Language Models and An Illustrated Guide to AI Agents. He also advises Outliers Venture Capital.[3]
Contributions
Draw the sequence of computations inside a transformer
The Illustrated Transformer walks through encoder and decoder stacks, embeddings, positional information and attention. It separates queries, keys and values, then shows how attention scores weight the information passed between positions. Multi-head attention lets different learned projections contribute different relationships. The result gives readers a visual route from a sentence to the matrix operations and residual layers inside the architecture.[4]
Build a component-by-component model of an LLM
The DeepLearning.AI course introduces tokenization, embeddings, self-attention and transformer blocks before assembling the parts into a language-model explanation. Alammar and Grootendorst use visual explanations to connect the architecture with generation rather than presenting attention as an isolated formula.[5]
Tools, writing & talks
Writing, research & talks
Start here
The Illustrated Transformerjalammar.github.ioA visual walkthrough of the original transformer architecture and its attention computations.
Start with the encoder and attention diagrams, then follow the numerical attention example. Basic vectors and matrix multiplication help; compare the original encoder–decoder architecture with the later LLM course.
Author[4]A DeepLearning.AI course taught with Maarten Grootendorst.
Co-instructor[5]Original source