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LLM Word of the Week: Attention

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Transformers

Attention is the core idea that changed AI forever. It’s how large language models decide what to focus on.

LLM Word of the Week: Attention

How models decide which words matter — and why it changed everything.

Before “Attention”: How NLP used to work

Before transformers, natural language processing (NLP) relied mostly on sequential models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks).
These models processed text word-by-word, predicting the next word based only on the previous few words — a bit like finishing a sentence without remembering the paragraph before it.

They worked, but struggled with long-term context. The further away a word was, the less influence it had. That’s why models often lost the thread in long sentences or documents.

What “Attention” really means

In the world of large language models, attention is the mechanism that lets a model “look at” different words (or tokens) and decide which ones are important as it generates a response.

Instead of processing text strictly in order, the model uses attention to weigh relationships across the entire input — allowing it to capture long‑range dependencies and subtle context.
(Source: Introduction to Transformers and Attention Mechanisms)

Why attention was the breakthrough

The 2017 paper Attention Is All You Need by Vaswani et al. introduced the Transformer Architecture — a model built entirely on attention (no recurrence or convolution).

Because of attention:

  • Models can analyze all parts of a sentence simultaneously.
  • Each token can decide how much influence other tokens have (via queries, keys, and values).
  • Training became massively parallelizable and scalable.

This idea powered everything from GPT to Gemini — it’s what made generative AI possible.

Why it matters for you

  • When you prompt an AI and it “gets the context” (even if key info was 50 words back), that’s attention at work.
  • It means models aren’t just “remembering recent text” — they’re reasoning across the full input.
  • It shapes how you should write prompts, structure context, and design systems.

Simple analogy

Think of reading a novel with many characters.
Without attention, the model reads linearly and forgets details from earlier chapters.
With attention, it’s like giving the model a board of sticky notes — it can look back at key moments and see how they connect to the current scene.

Final thought

If the architecture of modern LLMs is the house, attention is the wiring that connects every room.
It’s the reason models can go beyond predicting words — to actually understanding relationships.