The transformer is a neural network architecture, published in 2017, that now underpins practically all modern language models – from GPT to Claude and Gemini. Its core innovation is self-attention: each element of an input sequence can "look" directly at all others and weight their contribution to meaning.
This solved the problems of earlier sequence models such as LSTMs: long dependencies in text are learned better, training parallelises massively and models scale to billions of parameters. With that scale come today's capabilities – fluent language, reasoning, translation, code generation.
For recruiting applications, transformers are indirectly everywhere: embedding models, semantic search, resume parsers, role evaluations, cover-letter generators – all typically rely on transformer variants. Roughly understanding the architecture helps appraise strengths and weaknesses.
Lunigi uses transformer-based models in the backend, without requiring candidates to handle the technology directly.