Introduction
Prompt engineering is the art and science of formulating effective instructions for Large Language Models (LLMs). The quality of the output depends directly on the quality of the input.
Fundamental Techniques
1. Clear Role Instructions
Define the role the model should assume:
You are an experienced data protection officer with 10 years
of experience in GDPR consulting for medium-sized companies.
2. Structured Instructions
Break complex tasks into clear steps:
Analyse the following contract in three steps:
1. Identify all data protection-relevant clauses
2. Assess compliance with Art. 28 GDPR
3. Formulate specific improvement suggestions
3. Few-Shot Prompting
Provide examples of the desired output:
Create a risk assessment in the following format:
Example:
Risk: Data loss through unencrypted transmission
Severity: High
Probability: Medium
Measure: Implement TLS 1.3 for all API calls
Golden Rule
The more specific and structured your prompt is, the better and more consistent the result will be. Avoid vague instructions like "Do something good".
Advanced Techniques
Chain-of-Thought (CoT)
Ask the model to reveal its thinking process:
Think step by step and explain your reasoning
before reaching a conclusion.
Constraint-Based Prompts
Define clear boundaries and formats:
- Maximum length: "Answer in a maximum of 3 sentences"
- Format: "Answer as a JSON object"
- Language: "Answer exclusively in English"
- Tone: "Use formal, professional language"
Prompt Templates
Create reusable templates for frequent tasks:
| Template | Use Case |
|---|---|
| Compliance Check | Assessment against regulatory requirements |
| Summary | Condensation of long documents |
| Translation | Expert translation with context |
| Code Review | Security and quality review |
Anti-Patterns
Avoid these common mistakes:
- Too vague: "Help me with my project" → Better: Describe the specific problem
- Too long: Overly long prompts lose context → Better: Prioritise the essentials
- Contradictory: "Be creative, but follow the specifications exactly" → Better: Set clear priorities
- Without context: Missing background → Better: Provide relevant context
Data Protection in Prompts
Never submit personal data, trade secrets or confidential information in prompts to external AI services without first verifying the data protection permissibility.
Summary
Good prompt engineering is:
- Specific rather than vague
- Structured rather than chaotic
- Context-rich rather than context-free
- Iterative – test and improve
Professionelles Prompt Engineering fuer Ihr Team?
Creativate AI Studio unterstuetzt Sie bei der Entwicklung wiederverwendbarer Prompt-Architekturen, der Evaluation von KI-Ausgaben und dem Aufbau interner Best Practices fuer den produktiven KI-Einsatz.