Prompt Engineering Fundamentals

Prompt engineering is the art of crafting inputs that guide AI models to produce desired outputs. Master these fundamentals to dramatically improve your AI interactions.

The Anatomy of a Good Prompt

A well-structured prompt typically contains:

1. Context - Background information the model needs
2. Task - Clear description of what you want
3. Format - How you want the output structured
4. Constraints - Any limitations or requirements

Context: You are a senior software engineer reviewing code.
Task: Review this Python function for potential issues.
Format: Provide a bulleted list of findings with severity.
Constraints: Focus on security and performance only.

Key Principles

Be Specific, Not Vague

Provide Examples

Show the model what you want:

Convert these sentences to formal business English:

Input: "gonna need those reports asap"
Output: "I will require those reports at your earliest convenience."

Input: "cant make the meeting, something came up"
Output: [Your turn]

Use Role-Based Prompting

Assign a persona to get domain-specific responses:

You are an experienced DevOps engineer specializing in Kubernetes.
Explain how pod autoscaling works to a developer who has never
used container orchestration.

Common Patterns

The CRISP Framework

• Context: Set the scene
• Role: Define who the AI should be
• Instructions: What to do
• Specifics: Details and constraints
• Preview: Expected output format

The Chain Pattern

Break complex tasks into steps:

Let's solve this step by step:
1. First, identify the problem type
2. Then, list possible approaches
3. Evaluate each approach
4. Select and implement the best one
5. Verify the solution

Prompt Anti-Patterns

Avoid these common mistakes:

• Ambiguity: "Fix the bug" (which bug? how?)
• Overloading: Asking for too many things at once
• Assumptions: Assuming the model knows your context
• No constraints: Getting verbose responses when you need brief ones

Iteration Strategy

Prompts rarely work perfectly on the first try:

1. Start with a basic prompt
2. Analyze the response gaps
3. Add specificity where needed
4. Test with edge cases
5. Refine until consistent

Output Formatting

Control response structure:

Respond in the following JSON format:
{
  "summary": "one sentence summary",
  "key_points": ["point 1", "point 2"],
  "confidence": "high/medium/low"
}

Temperature and Parameters

When using APIs, understand these settings:

For code generation, use low temperature (0.1-0.3). For creative writing, try higher values (0.7-0.9).