The lecture showed how to get practical, relevant outputs from ChatGPT without unnecessary theory. The key is specific prompts, clear context, role, structure, and sensible constraints, complemented by experience from real-world office practice and closed enterprise deployments.
Context, role, and structure lead to better answers
State who the answer is for and what you want to use it for: "I'm a layperson, explain blockchain simply and suggest how I can use it in decision-making." This gives the model a compass, and the response will be both understandable and practical. Assigning a role also helps, for example, "you are an AI expert for small and medium-sized businesses in logistics," which narrows the perspective and increases the relevance of the advice. A requested structure ("introduction, body, conclusion") will also guide the output by giving the model a framework for argumentation; and by the way, politeness in communication never hurts.
Constraints and closed deployments in practice
Constraints like "create a catchy eight-word slogan" or "answer in up to ten sentences" keep the output concise and usable. In practice, closed chatbots have also proven effective: for example, for an association of chemists a helper was created that draws only from the provided internal documents, not from the web, and answers in the context of their content. Similarly, it helps with office routines: from a long Excel expense table it can sort invoices paid by card versus bank transfer and prepare a summary for a quick review. The takeaway? Be clear, provide context and a role, define structure and boundaries — and you will get more precise, faster, and more useful results.
