A prompt engineer designs, tests and refines the instructions and context given to AI models so they produce accurate, consistent and safe output. For buyers, the key question is whether that work needs a dedicated role or sits within AI engineering.
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.
An evaluation set is a fixed collection of inputs with expected outcomes, used to score an AI system the same way every time it changes. It plays the role a regression test suite plays for ordinary code.
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.
Prompt engineering is the practice of designing and testing the instructions, context and examples given to a language model. The aim is reliable, useful output.
An AI product manager decides what AI-powered features to build and how to judge them, balancing user needs with model capabilities, data, evaluation and risk. They define success when outputs are probabilistic rather than fixed.
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