Prompt Engineering
Explore how to design, manage, and govern prompts for generative AI foundation models. Learn various prompt engineering techniques like zero-shot, few-shot, and chain-of-thought, plus best practices and governance to ensure predictable, safe, and scalable AI applications.
As foundation models become central to modern applications, the way we communicate with them becomes just as important as the models themselves. Prompt engineering is the discipline of designing, managing, and governing the instructions we give to foundation models so that their responses are accurate, consistent, safe, and aligned with application goals. Rather than an ad hoc activity, prompt engineering in production environments is a structured, iterative process that combines design, testing, governance, and observability.
What is prompt engineering?
A prompt is the input we provide to a generative AI foundation model to guide its behaviour. Prompts are composed of structured instructions, contextual data, formatting constraints, and behavioral guidelines that work together to shape model output.
Prompt engineering is the practice of deliberately designing these components to achieve predictable outcomes. This includes deciding what information the model needs, how to structure that information, and which constraints to apply to the output. As models become more capable, prompt engineering shifts from simple phrasing tweaks to system-level design decisions that affect reliability, performance, and safety.
Model latent space
The latent space of a model refers to the internal representations it builds to process and generate output. When we input a prompt into a model, the model encodes it as a vector in its latent space, where it internally represents relationships among words, concepts, and tasks.
While this is a more technical concept, it’s important to recognise that the quality and clarity of our prompt can ...