Transformer
The neural network architecture behind most modern large language models, using a mechanism called self-attention to understand context across long sequences of text.
In plain English
A transformer is the underlying design used by most modern AI language models. It lets the model pay attention to every word in a sentence at once, so it understands meaning and context much better than older approaches.
Technical definition
The transformer is a sequence-to-sequence architecture built on multi-head self-attention and feed-forward layers. Each self-attention head computes scaled dot-product attention over all positions simultaneously, allowing the model to capture long-range dependencies without sequential processing. Positional encodings add order information since the architecture itself is permutation-invariant.
Business use case
Businesses rely on transformer-based models for summarisation, sentiment analysis, customer support automation, code generation, and document understanding. Understanding transformers helps explain why these tools are strong at language and why context window size affects what you can process in one call.
Example
GPT-4, Claude, and Gemini are all transformer-based large language models. When you ask one of them to summarise a document, the transformer architecture allows it to attend to every part of the document at once to produce a coherent answer.
Frequently asked questions
A transformer is a type of neural network architecture introduced in 2017. It uses self-attention to weigh the importance of each word relative to every other word in a sequence, allowing it to understand context at scale far better than earlier models.
Recurrent neural networks processed text word by word and struggled with long sequences. Transformers process the entire sequence in parallel and scale efficiently with more data and compute, which enabled today's large language models.
Not technically. Knowing what they are helps you understand why LLMs excel at language tasks and have context limits, but you do not need to study the math to use AI tools productively.
No. The transformer architecture has been adapted for images (Vision Transformers), audio, protein sequences, and time-series data, making it a general-purpose approach across many AI domains.
Keep exploring
Large Language Model
A large language model is an AI trained on huge amounts of text so it can read your question and write a useful answer. It powers chatbots and writing assistants.
Neural Network
A neural network is a computer model inspired by how brain cells connect. It learns by adjusting many tiny connections until it can recognize patterns, like telling cats from dogs in photos.
Embeddings
Embeddings turn words, sentences, or images into lists of numbers that capture their meaning. Things with similar meaning get similar numbers, so a computer can tell what is related.
Put AI intelligence to work in your business
Sitebard AI brings together the data, guides, and career intelligence you need to make confident AI decisions.