This chapter lifts the hood on the artificial intelligence (AI) that is learning to speak the language of biology. We demystify the revolution from classical machine learning, which relied on handcrafted rules, to the deep learning paradigm where models discover complex biological patterns on their own through “representation learning”. At the peak of this revolution is the Transformer, the architectural marvel powering today’s Large Language Models (LLMs). We deconstruct its core engine, the self-attention mechanism, which enables models to understand context across vast biological sequences, connecting a distant gene enhancer to its promoter or a subtle effect to its underlying cause. We then follow the complete LLM lifecycle, from the pre-training on massive data corpora that builds foundational knowledge, to fine-tuning techniques (like SFT and RLHF) that sculpt these models into specialized domain experts for genomics, drug discovery, or clinical medicine. This chapter provides a technical primer on AI, moving beyond the prevalent media hype. It establishes the foundational vocabulary and conceptual framework necessary to critically evaluate and effectively harness these powerful technologies.