What are LLMs? How large language models actually work

Large language models are transformer-based neural networks trained primarily to predict the next token in text, a capability that supports tasks such as writing, coding, translation, summarization, question answering, and tool use. They process text as tokens, convert tokens into numerical embeddings, and use self-attention to interpret relationships across context, with model capability influenced not only by parameter count but also by architecture, data quality, and post-training. Development generally progresses from large-scale self-supervised pretraining to supervised fine-tuning, preference optimization, and, increasingly, reasoning-focused training. During inference, models generate tokens sequentially, making prompt processing, decoding speed, memory management, caching, batching, speculative decoding, and quantization important production concerns. Developers may choose closed API-based models or open-weight models that offer greater control, customization, and deployment flexibility but require more operational expertise. Because LLMs can hallucinate, rely on outdated training knowledge, lose information in long contexts, reproduce biases, and perform poorly on precise arithmetic, production applications commonly add retrieval, tools, output constraints, verification, and evaluation systems around the model.

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