Definition: A large language model (LLM) is a neural network trained on very large amounts of text to predict the next token, which lets it understand instructions and generate, summarize, translate and reason over language and code.
Most LLMs use the transformer architecture. During pretraining the model learns statistical patterns of language from vast text; further training with examples and human feedback teaches it to follow instructions. At run time it reads a prompt within its context window and generates a reply one token at a time.
LLMs are flexible across many tasks without task-specific training. They can also produce hallucinations, lack knowledge of private or recent data unless it is supplied, and vary in cost and latency by model size.
Companies usually call hosted models from providers such as OpenAI or Anthropic, or run open-weight models in their own cloud. Engineering work focuses on prompt engineering, retrieval, function calling, evaluation and, when needed, fine-tuning.
Ryz LLM engineers build LLM applications on models from Anthropic, OpenAI and others in your stack, working on your team.
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