What is prompt engineering?

Why the prompt matters

A large language model only knows what it learned in training and what is in the prompt. It does not know your company, your customers or your quality standards. The same question can therefore produce a vague or a very precise answer depending on how it is phrased. Good prompts reduce rework, misunderstandings and hallucinations.

Building blocks of a good prompt

• Role and goal: what should the model do, and for whom? Example: "You are preparing an offer for an existing customer in mechanical engineering."
• Context: relevant information, documents, previous decisions.
• Task: a concrete, verifiable instruction instead of a vague wish.
• Constraints: length, tone, language, what must not be included.
• Output format: list, table structure, e-mail, JSON – whatever you need next.
• Examples: one or two samples of a good result show the model the expected standard.
• Uncertainty: explicitly allow the model to say when information is missing.

Proven techniques

1. Step by step: have complex tasks broken down into individual steps, or split them into several prompts.
2. Ask for questions first: let the model ask clarifying questions before it starts.
3. Demand sources: when working with company knowledge, ask for the passages used – this is where RAG comes in.
4. Iterate: treat the first answer as a draft and refine it with targeted feedback.
5. Reuse: save prompts that work well as templates instead of rewriting them every time.

From individual prompt to team standard

In companies, prompt engineering is less about clever tricks than about shared standards. When every employee writes their own prompts, results vary greatly. Prompt templates with variables – for example for customer name, product or deadline – make good formulations reusable and lower the entry barrier for less experienced users. The next step is to embed proven prompts in AI agents that run them with fixed instructions and tools.

Limits

• A good prompt does not replace missing data: without access to the right documents, the model can only guess.
• Prompts are model-dependent; a template may need adjustment after a model change.
• Confidential data does not belong in prompts sent to providers without a data processing agreement.

Prompt engineering in AIPE

AIPE has a prompt library: prompt templates with variables can be created, used in chat and shared with the team. Agents can be given fixed instructions, documents and tools; routines run prompts on a schedule or once. Answers based on company knowledge reference the source documents. An overview of the functions is on the features page; the guide AI in the Mittelstand explains how to introduce AI in teams.