Large Language Models learn statistical patterns from text. Instead of memorizing entire documents, they learn how likely words are to appear in certain contexts. This allows them to predict and generate text one token at a time.
Transformers introduced self-attention, a mechanism that lets the model weigh the importance of different words in a sentence relative to each other. For example, in the sentence 'The cat sat on the mat because it was warm', attention helps map 'it' to 'mat'.
Embeddings convert words and sentences into numeric vectors. Cosine similarity between embeddings provides a measure of semantic relatedness. This is used in search, clustering, and retrieval-augmented generation.
Training involves next-token prediction using massive datasets. Techniques like masking, curriculum learning, and fine-tuning on task-specific data help the model generalize and specialize.
In practice, LLMs power chatbots, summarization systems, and code assistants. Good prompts, system instructions, and retrieval strategies are key to reliable results.
Responsible use includes data governance, bias evaluation, and safety layers. Human oversight remains essential in high-stakes settings.