Claude Prompting

Claude Prompt Engineering

Claude is Anthropic's AI assistant designed to be helpful, harmless, and honest. It excels at long-context tasks, follows complex instructions precisely, and works best with well-structured prompts using XML-style tags.

When to Invoke This Skill

Use this skill when:

• Crafting prompts specifically for Claude/Anthropic models (default: Claude Sonnet 4.5)
• Working with long documents or large context (up to 1M tokens with Sonnet 4.5 beta)
• Using structured prompts with XML-style tags
• Implementing extended thinking for complex reasoning
• Requiring precise instruction following
• Building agentic workflows with parallel tool use

Claude's Identity & Characteristics

Universal Prompting Techniques (Claude-Adapted)

1. Zero-Shot Prompting

Claude responds well to clear, direct zero-shot prompts.

Good Example:

Extract the key dates and events from the following text:

<text>
[paste text]
</text>

Output format: JSON with keys "date", "event", "description"

Less Effective:

Can you tell me what dates are in this text?

2. Few-Shot Prompting (Multishot)

Use well-formatted examples with XML structure.

<examples>
<example>
<input>
The conference is scheduled for March 15, 2025 in San Francisco.
</input>
<output>
{
  "date": "2025-03-15",
  "event": "conference",
  "location": "San Francisco"
}
</output>
</example>

<example>
<input>
Our next board meeting is on June 22nd.
</input>
<output>
{
  "date": "2025-06-22",
  "event": "board meeting"
}
</output>
</examples>

<input>
The product launches on September 1st in New York.
</input>

<output>

3. Chain-of-Thought Prompting

Claude has an Extended Thinking feature that shows reasoning (enabled via API, output in response):

I need to decide between these two job offers. Let me think through this step by step.

<job_offer_a>
[details]
</job_offer_a>

<job_offer_b>
[details]
</job_offer_b>

Please analyze both offers, show your reasoning, and provide a recommendation.

API enables extended thinking; response includes:

<thinking>
First, let me analyze the compensation...
Then consider the growth potential...
The work-life balance factors are...
The company stability differs by...
</thinking>

<answer>
[conclusion]
</answer>

4. Zero-Shot CoT

Simply add "Let's think step by step" or similar:

What's the most efficient route to visit all these cities?

Let's think step by step.

5. Prompt Chaining with XML Tags

Break complex tasks using XML delimiters:

Chain Step 1:

<task>
Extract relevant quotes from this document related to [topic].
</task>

<document>
[paste document]
</document>

<output_format>
<quotes>
<quote>[relevant quote 1]</quote>
<quote>[relevant quote 2]</quote>
</quotes>
</output_format>

Chain Step 2:

<task>
Summarize the extracted quotes and synthesize key insights.
</task>

<quotes>
[from previous response]
</quotes>

<output_format>
<summary>
[executive summary]
</summary>

<key_insights>
<insight>[insight 1]</insight>
<insight>[insight 2]</insight>
</key_insights>
</output_format>

6. ReAct Prompting

Use structured thought-action-observation cycles:

<question>
[research question]
</question>

<thought_1>
[what needs to be done first]
</thought_1>

<action_1>
[tool use or information gathering]
</action_1>

<observation_1>
[result from action]
</observation_1>

<thought_2>
[next step based on observation]
</thought_2>

<final_answer>
[conclusion]
</final_answer>

7. Tree of Thoughts

Use multiple reasoning paths with XML structure:

<problem>
[complex problem]
</problem>

<thought_paths>
<path_1>
<assumption>[approach 1]</assumption>
<reasoning>[step-by-step]</reasoning>
<conclusion>[result]</conclusion>
</path_1>

<path_2>
<assumption>[approach 2]</assumption>
<reasoning>[step-by-step]</reasoning>
<conclusion>[result]</conclusion>
</path_2>

<path_3>
<assumption>[approach 3]</assumption>
<reasoning>[step-by-step]</reasoning>
<conclusion>[result]</conclusion>
</path_3>
</thought_paths>

<synthesis>
[best path and why]
</synthesis>

Claude-Specific Best Practices

1. Use XML-Style Tags for Structure

Claude's official courses extensively use XML tags:

<context>
[background information]
</context>

<task>
[what needs to be done]
</task>

<examples>
[example inputs and outputs]
</examples>

<input>
[the actual input to process]
</input>

<output_format>
[expected format]
</output_format>

2. Structure Long Prompts Hierarchically

From Anthropic's official courses:

[TASK_CONTEXT]
Setting the stage and overall context

[TONE_CONTEXT]
How Claude should approach the task

[INPUT_DATA]
The actual data to work with

[EXAMPLES]
Few-shot examples

[TASK_DESCRIPTION]
Specific task details

[IMMEDIATE_TASK]
The immediate action to take

[OUTPUT_FORMATTING]
Expected output structure

3. Leverage Extended Thinking

For complex reasoning, enable Claude's extended thinking via the API:

API Syntax (Python SDK):

response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    thinking={
        "type": "enabled",
        "budget_tokens": 8192
    },
    messages=[{
        "role": "user",
        "content": "Analyze this complex problem..."
    }]
)

Prompt-Side (XML structure for expected output):

<task>
[complex reasoning task]
</task>

<thinking>
[Claude will show its reasoning here]
</thinking>

<answer>
[final answer]
</answer>

Key Points:

• budget_tokens sets max tokens for reasoning (must be < max_tokens)
• Claude 4.5 returns summarized thinking by default
• First few lines are more verbose (useful for prompt engineering)
• You're billed for full thinking tokens, not summary tokens

4. Use System Prompts Effectively

System prompts set Claude's behavior:

System: You are a technical writer specializing in API documentation. Your responses are always:
- Clear and concise
- Technically accurate
- Formatted with Markdown
- Focused on developer needs

User: [your actual query]

5. Prefill Claude's Response

Guide the format by starting Claude's response:

<task>
Analyze this document and extract key findings.
</task>

<document>
[paste document]
</document>

<response>
<summary>
[Claude continues from here]

6. Cache Control for Long Prompts

Optimize for repeated prompts:

<cached_content cache_control="{\"type\":\"ephemeral\"}">
[large context that doesn't change]
</cached_content>

<task>
[specific task that varies]
</task>

7. Claude 4.5 Agent Features

Claude 4.5 introduces powerful agent capabilities:

Parallel Tool Use - Claude can use multiple tools simultaneously:

<task>
Analyze this data and create a visualization.
</task>

<tools>
- Web search for market data
- Code execution for analysis
- File write for chart output
</tools>

Claude will execute these in parallel when possible.

Memory Files - Claude can maintain knowledge across sessions:

<task>
When working on ongoing projects, create a memory file to track:
- Key decisions and rationale
- Project context and constraints
- Preferences and patterns
</task>

Claude will automatically update and reference memory files when given local file access.

Extended Thinking with Tools - Reasoning can pause to use tools:

# API: Enable extended thinking with tool use
response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    thinking={
        "type": "enabled",
        "budget_tokens": 8192
    },
    tools=[web_search_tool],
    messages=[{
        "role": "user",
        "content": "Research [topic] and provide a comprehensive analysis."
    }]
)

# Claude can now use web search DURING extended thinking,
# alternating between reasoning and information gathering.

Anti-Patterns to Avoid

Quick Reference Templates

Document Analysis

<task>
[specific analysis task]
</task>

<document>
[paste document]
</document>

<output_format>
[expected structure]
</output_format>

Code Generation with Examples

<task>
Write a function that [description]
</task>

<requirements>
[specific requirements]
</requirements>

<examples>
<example>
<input>[input example]</input>
<output>[expected output]</output>
</example>
</examples>

<output_format>
[code in specified language]
</output_format>

Data Extraction

<task>
Extract [specific fields] from the following text
</task>

<input_text>
[paste text]
</input_text>

<output_format>
JSON with keys: [list keys]
</output_format>

Extended Thinking

# API: Enable extended thinking
response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    thinking={
        "type": "enabled",
        "budget_tokens": 8192
    },
    messages=[{
        "role": "user",
        "content": """
<task>
[complex reasoning task]
</task>

Please show your reasoning step by step, then provide the final answer.
"""
    }]
)

# Response will include <thinking> block followed by <answer>

Model Capabilities Reference

Recommendation: Start with Claude Sonnet 4.5 - best balance of intelligence, speed, and cost for most use cases. Use Opus 4.5 for complex coding, Haiku 4.5 for speed-critical tasks.

Migration Notes (Claude 3 → 4.5)

If you're migrating from Claude 3.x to Claude 4.5:

API Migration:

# Old (Claude 3.5)
model="claude-sonnet-3-5-20240620"

# New (Claude 4.5)
model="claude-sonnet-4-5-20250929"  # or use alias "claude-sonnet-4-5"

Most prompting techniques remain unchanged—XML tags, system prompts, and structured outputs work identically.

Prompt Element Checklist

When creating Claude prompts, consider including:

• Task Context: Overall purpose and setting
• Tone Context: How Claude should approach it
• Input Data: The actual content to process
• Examples: Few-shot demonstrations (if needed)
• Task Description: Specific instructions
• Immediate Task: What to do right now
• Output Format: Expected structure
• Prefill: Start of Claude's response (optional)

See Also

• references/basics.md - Foundational Claude prompting concepts
• references/techniques.md - Detailed technique explanations
• references/xml-formatting.md - XML tag patterns and usage
• references/patterns.md - Reusable Claude prompt patterns
• references/examples.md - Concrete examples from Anthropic courses
• grok-prompting skill - For Grok/xAI-specific guidance
• gemini-prompting skill - For Google Gemini-specific guidance