A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
Fine-tuning is further training of an existing model on a smaller, task-specific dataset so it adopts a particular style, format or skill. It changes how a model behaves without the cost of training a new one from scratch.
LLMOps is the practice of running applications built on large language models in production, from prompt versioning to evaluation and monitoring. It extends MLOps for systems whose behaviour depends on prompts, retrieved context and external models.
Retrieval-augmented generation (RAG) is a pattern that hands a language model relevant passages from your own documents at question time. Its answers then rest on that material, not only on what it learnt in training.
A transformer is a neural network architecture that uses attention to weigh the relationships between all parts of an input. It is the basis of most modern language models.
An AI agent is software that wraps a language model in a loop with tools and memory so it can work toward a goal. It chooses an action, reads the result and continues until the task is done.
An AI engineer builds software products and features on top of AI models, especially large language models, using prompting, retrieval, tool use and evaluation. Companies hire one to turn a promising AI demo into a dependable product.
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