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AdvancedLesson 1 of 510 min read

The Transformer: the architecture that changed everything

A course from the Academy: Under the hood of LLMs

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In 2017, a research paper titled "Attention Is All You Need" introduced the Transformer. All modern generative AI, GPT, Claude, Gemini, image generators, descends from this paper. Understanding its core idea is understanding the era.

The problem before

Previous language models read text word by word, trying to remember what came before. The result: over a long sentence, the beginning would fade, and crucially, it was impossible to parallelise the computation efficiently.

The brilliant idea: attention

The Transformer looks at all words at once, and computes for each word attention scores: how relevant every other word in the text is for interpreting it. In "The key I had placed on the table has disappeared, it was golden," the attention mechanism strongly links "it" and "golden" to "key," despite the distance. Each word thus builds its representation by "consulting" the words that matter to it, across dozens of successive layers.

Why it triggered everything

  • Parallelisable: all words are processed at once → perfect for GPUs → giant training runs become possible.
  • Long range: links between distant words are no longer lost.
  • General-purpose: the same architecture works for text, images (split into patches), audio, proteins…
Analogy. The old method read a detective novel page by page, hoping to remember everything. The Transformer is the detective's board: all clues displayed at once, linked by red strings, each clue illuminated by all the others, no matter which chapter it appeared in.

Key takeaways

  • The Transformer (2017) is the foundational architecture of all generative AI.
  • Attention = each word weighs the relevance of all others.
  • Its parallelisation made internet-scale training possible.

Key concepts

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The course: Under the hood of LLMs

  1. The Transformer: the architecture that changed everything10 min
  2. From raw text to assistant: fine-tuning and RLHF10 min
  3. Embeddings and RAG: connecting AI to your data10 min
  4. Mixture of Experts, distillation, quantization: the engineering of efficiency9 min
  5. Evaluating an AI: benchmarks, limits, and critical thinking9 min

The whole Academy →The AI glossary