/anthropic-prompt-engineer

Core Prompt Engineering Techniques

Anthropic's official prompt engineering techniques for Claude AI models.

1. Be Clear and Direct

What it is: Provide explicit, unambiguous instructions

Why it works: Claude 4.x models are trained for precise instruction following

When to use:

• Any prompt where clarity matters
• Production systems requiring consistent output
• Complex tasks with specific requirements

How to apply:

• State exactly what you want
• Specify format, length, and tone
• Avoid vague or ambiguous language
• Include specific constraints and requirements

Examples:

❌ Bad:

Tell me about Python

✅ Good:

Write a 300-word explanation of Python's list comprehensions for intermediate developers.
Include:
- 3 practical examples
- Performance considerations
- Common pitfalls to avoid

2. Use XML Tags for Structure

What it is: Organize prompts with XML tags like <instructions>, <example>, <context>

Why it works: Claude was trained with XML tags, naturally recognizing them as structural elements

When to use:

• Separating data from instructions
• Complex prompts with multiple sections
• When working with variable content
• Production systems requiring clear boundaries

Common tags:

• <instructions> - Main task description
• <example> - Few-shot examples
• <context> - Background information
• <document> - Input data to process
• <output_format> - Expected result format
• <constraints> - Limitations and rules

Example:

<instructions>
Analyze the customer review below and extract structured sentiment data.
</instructions>

<review>
The product arrived damaged and customer service was unhelpful.
</review>

<output_format>
Return JSON with:
- "sentiment": "positive" | "negative" | "neutral"
- "confidence": 0.0 to 1.0
- "key_issues": array of strings
</output_format>

3. Chain of Thought (CoT)

What it is: Ask Claude to think step-by-step before providing final answer

Why it works: Breaking down problems leads to more accurate, nuanced, and reliable responses

When to use:

• Math and logical reasoning problems
• Complex analysis tasks
• Multi-step processes
• When accuracy is critical
• Debugging and troubleshooting

How to apply:

• Add "Think step by step" to your prompt
• Request reasoning in <thinking> tags
• Ask Claude to show its work
• Guide the thinking process with specific steps

Examples:

Basic CoT:

Solve this problem step by step:

<problem>
A store sells apples for $2 each. If you buy 5 or more, you get 20% off.
How much do 7 apples cost?
</problem>

Think through this step by step, showing your work.

Structured thinking:

Analyze this code for bugs. Use the following process:

<thinking>
1. Read and understand the code structure
2. Identify potential issues
3. Assess severity of each issue
4. Recommend fixes
</thinking>

<code>
[code here]
</code>

4. Prefilling Claude's Response

What it is: Provide the beginning of Claude's response to guide output format and style

Why it works: Immediately directs the response in the desired direction

When to use:

• Forcing specific output formats (especially JSON)
• Controlling tone and style
• Ensuring responses start correctly
• Avoiding preambles

Examples:

JSON output:

User: Extract the name, email, and phone from this text: [text]