What is Transformer? Definition & Guide

What is Transformer?

he transformer is the neural network architecture introduced in the 2017 paper 'Attention Is All You Need' that powers every modern large language model. Transformers use a self-attention mechanism to relate every token in a sequence to every other token, capturing long-range dependencies that earlier RNN architectures missed. GPT, BERT, T5, and all modern LLMs are transformer-based architectures.

The self-attention mechanism is the transformer's key innovation. For each token in the input sequence, the attention mechanism computes a weighted sum of all other tokens' representations, where the weights reflect how relevant each other token is to understanding the current token. This allows the transformer to capture long-range dependencies directly — 'bank' in 'the river bank was steep' attends strongly to 'river', while 'bank' in 'I went to the bank' attends strongly to context about money. Earlier RNN architectures processed text sequentially and struggled to maintain relevant context over long sequences; transformers process the entire sequence in parallel and can attend to any position directly.

Two variants of the transformer architecture dominate modern enterprise AI. Encoder-only transformers (BERT-style) process input sequences in both directions simultaneously — well-suited for understanding tasks like classification, extraction, and embedding generation. Decoder-only transformers (GPT-style) process sequences left-to-right and generate text one token at a time — well-suited for generation tasks like conversation, summarisation, and question answering. Modern frontier LLMs are almost exclusively decoder-only transformers with architectural refinements (grouped-query attention, rotary positional embeddings, mixture-of-experts) that improve efficiency at scale.

Also known as: Transformer Architecture, Attention Model

Key Points

Core idea
The self-attention mechanism lets every token attend to every other token in the sequence — directly capturing long-range semantic relationships that sequential architectures like RNNs couldn't handle reliably.
Why it matters
Encoder-only transformers (BERT) are optimal for understanding tasks (embedding, classification). Decoder-only transformers (GPT, Llama) are optimal for generation. Most frontier LLMs are decoder-only.
Enterprise use
Unlike sequential RNNs, transformers process entire sequences in parallel — enabling training on massive datasets with the compute efficiency needed to reach the scale that produces emergent capabilities.

How It Works

How Transformer works

Define the purpose, inputs, and success criteria that Transformer must support.
Apply Transformer in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

Transformer-based models on enterprise infrastructure.

Fluid AI deploys transformer-based LLMs on customer hardware, covering both open-weight models like Llama and Mistral and private fine-tuned variants optimised for regulated industry tasks.

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Topics Covered

transformer architecture LLM
self-attention mechanism explained
encoder decoder transformer enterprise
transformer neural network explained
how transformers work
attention mechanism NLP
transformer vs RNN enterprise AI
GPT transformer architecture

Transformer