What is prompt engineering?

Prompt engineering: definition

A prompt is the input to an AI model: a question, an assignment or a longer set of instructions. Prompt engineering refers to the systematic design, testing and improvement of such inputs. The goal is for a language model to deliver the desired answer in the desired form not just occasionally but repeatably.

In everyday use, prompts are often short; in business applications they are part of the software. A so-called system prompt defines how an assistant behaves, what it may do and what it must stick to. Good prompts are therefore maintained, versioned and tested like source code.

Building blocks and techniques

A solid prompt usually names the role (“You are a service advisor in mechanical engineering”), the task, the necessary context, constraints and the output format. Examples of good answers, known as few-shot examples, help the model match style and structure. For evaluations, a fixed format such as JSON can be specified.

For complex tasks it pays to specify working steps or to break the task into several small prompts. In addition, the model is given relevant company knowledge, for example through retrieval-augmented generation (RAG), instead of being left to guess. This turns a single prompt into a robust process whose intermediate results can be checked.

Real-world example: draft replies in customer service

A garden equipment retailer wants to answer customer emails faster. The first prompt, “Answer this email in a friendly way,” produces uselessly generic texts. Only with a role, company tone, shipping terms, three sample replies and the rule “For complaints, never promise goodwill gestures; hand over to service” do drafts emerge that can be sent with small changes. After a familiarization period, employees had to make fewer corrections and could focus on cases that really need a personal reply.

The team also collected test emails with the desired reply. With every change to the prompt, this test series ran again. That kept quality traceable, and new wording did not degrade existing cases unnoticed. This way you quickly see whether a change is really better or just looks good in a single case.

Typical mistakes and imple­menta­tion

Common mistakes are vague assignments, too many tasks in one prompt and missing test cases. There is also a security aspect: with so-called prompt injection, third parties try to smuggle instructions into inputs or documents. Secrets such as passwords therefore never belong in a prompt.

Nano Concepts develops and tests prompts as part of AI applications and protects them with technical safeguards so that the assistant stays within its intended limits. Good prompts are not a one-off project: they need adjusting when products, prices or models change.

• Describe task, audience and output format clearly
• Provide examples of good and bad answers
• One task per prompt; split complex processes into steps
• Version prompts and check them with test cases
• Do not store credentials or confidential data in the prompt