Prompt Engineering

When someone gets a poor result from an AI tool, the instinct is often to assume the model isn't good enough. Frequently the actual problem is the instruction; it's vague, missing context, or not specifying what a good answer looks like. Prompt engineering is the practice of structuring those instructions deliberately: giving the model a role, a task, the right background, constraints on what to include or avoid, and a format for the output. It doesn't require technical skill, and it doesn't change the underlying model; it just changes what the model is working with when it generates a response.

For most employees, prompt engineering is the difference between an AI tool that feels unreliable and one that becomes genuinely useful, which makes it a worthwhile investment in training and shared tooling. For leaders, the more important question is knowing when a prompt is the right solution and when it isn't. A well-written prompt can make individual tasks faster and outputs more consistent. It cannot fix a model that lacks the right knowledge, a workflow that needs real data retrieval, or a deployment that needs security controls. Understanding that boundary helps executives approve the right level of investment — prompt libraries and training for productivity use cases, proper architecture for anything that touches sensitive data or consequential decisions.