Prompt Engineering

Prompt Engineering

Comprehensive prompt engineering guidance covering Claude (Anthropic), Gemini (Google), and Grok (xAI). Each model has unique strengths and optimal prompting patterns.

When to Invoke This Skill

Use this skill when:

• Crafting prompts for Claude, Gemini, or Grok models
• Leveraging model-specific capabilities (XML tags, system instructions, real-time knowledge)
• Working with long-context tasks (up to 1M+ tokens)
• Implementing extended thinking or reasoning features
• Building agentic workflows with parallel tool use
• Designing prompts for multimodal inputs (text, images, audio, video)

Quick Model Selection

Universal Prompting Techniques

These techniques work across all models with adaptations noted below.

1. Zero-Shot Prompting

Claude (XML-style):

<task>
Extract the key dates and events from the following text:
</task>

<text>
[paste text]
</text>

<output_format>
JSON with keys "date", "event", "description"
</output_format>

Gemini (System Instructions):

{
  "system_instruction": {
    "parts": [{"text": "You are a data extraction specialist. Output JSON only."}]
  },
  "contents": [{"parts": [{"text": "Extract dates from: [text]"}]}]
}

Grok (Conversational):

Hey, can you pull out all the important dates from this text and give me a JSON summary?

[text]

2. Few-Shot Prompting (Multishot)

Claude:

<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>
</example>
</examples>

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

<output>

Gemini: Similar structure with system instruction for classification task.

Grok: Provide natural language examples matching conversational tone.

3. Chain-of-Thought Prompting

Claude (Extended Thinking - API):

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>\n\nPlease show your reasoning step by step."
    }]
)
# Response includes <thinking> block followed by <answer>

Gemini (Thinking Config):

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Solve step-by-step: [problem]",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=8192)
    )
)

Grok (Zero-Shot CoT):

Let's work through this step by step:

[complex problem]

Walk me through your reasoning.

4. Prompt Chaining

Break complex tasks into sequential steps.

Claude (XML structure):

<!-- Step 1 -->
<task>Extract relevant quotes about [topic].</task>
<document>[text]</document>

<!-- Step 2 -->
<task>Summarize extracted quotes and synthesize key insights.</task>
<quotes>[from step 1]</quotes>

Gemini: Use long context (1M+ tokens) to maintain conversation history.

Grok: Natural conversation flow with follow-up questions.

5. ReAct Prompting

Thought-Action-Observation cycles for reasoning with tools.

Claude (XML):

<question>[research question]</question>

<thought_1>[what needs to be done first]</thought_1>
<action_1>[tool use]</action_1>
<observation_1>[result]</observation_1>

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

Gemini: Similar structure with system instruction for reasoning behavior.

Grok: Conversational "Let's approach this methodically" style.

6. Tree of Thoughts

Explore multiple reasoning paths before concluding.

Claude:

<thought_paths>
<path_1>
<assumption>[approach 1]</assumption>
<reasoning>[step-by-step]</reasoning>
<conclusion>[result]</conclusion>
</path_1>
<path_2>...</path_2>
</thought_paths>

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

Grok (Creative exploration):

Let's imagine three different perspectives on [topic]:
1. The Optimist's View: ...
2. The Skeptic's View: ...
3. The Pragmatist's View: ...

Where do these perspectives intersect?

Model-Specific Best Practices

Claude (Anthropic)

Key Features:

• XML-style tags for structure (official Anthropic recommendation)
• Extended thinking with tool use support
• Parallel tool execution
• Memory files for persistent knowledge
• Up to 1M tokens context (Sonnet 4.5 beta)

Prompt Structure:

[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

Best Practices:

• Use XML tags liberally for structure
• Enable extended thinking for complex reasoning
• Leverage parallel tool use
• Use cache control for repeated prompts
• Prefill responses to guide format

Gemini (Google)

Key Features:

• System instructions (primary behavior control)
• Native multimodal (text, images, audio, video, code)
• Ultra-long context (1M+ tokens)
• JSON schema validation for structured output
• Function calling for agent building

System Instruction Template:

<role>
You are Gemini, a specialized assistant for [Domain].
You are precise, analytical, and persistent.
</role>

<instructions>
1. Plan: Analyze the task and create a step-by-step plan.
2. Execute: Carry out the plan.
3. Validate: Review your output against the user's task.
4. Format: Present the final answer in the requested structure.
</instructions>

<constraints>
- Verbosity: [Low/Medium/High]
- Tone: [Formal/Casual/Technical]
</constraints>

<output_format>
Structure your response as follows:
1. Executive Summary: [Short overview]
2. Detailed Response: [The main content]
</output_format>

Best Practices:

• Always use system instructions for behavior
• Leverage multimodal inputs (combine text + images)
• Use JSON schema for structured outputs
• Process large documents with 1M+ context
• Enable thinking_budget for complex tasks

Grok (xAI)

Key Features:

• Witty, rebellious personality
• Real-time X (Twitter) knowledge access
• Relaxed content filtering
• Natural, conversational style

Prompt Style:

Conversational, direct, and flexible. Grok responds best to natural language rather than over-structured prompts.

Best Practices:

• Use conversational language (not overly formal)
• Leverage real-time knowledge for current events
• Embrace the wit (work with it, not against it)
• Be direct—no need for delicate prompting
• Explore controversial or filtered topics

Model Capabilities Comparison

Anti-Patterns to Avoid

Quick Reference Templates

Document Analysis (Claude)

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

<document>
[paste document]
</document>

<output_format>
[expected structure]
</output_format>

Multimodal Input (Gemini)

{
  "contents": [{
    "parts": [
      {"text": "Describe what's in this image."},
      {"inline_data": {"mime_type": "image/jpeg", "data": "[base64]"}}
    ]
  }]
}

Current Events Query (Grok)

What's the latest on [topic] according to posts on X? Give me a summary of the current sentiment.

Model Selection Guide

See Also

Reference Documentation

• references/basics.md - Foundational prompt engineering concepts
• references/techniques.md - Detailed technique explanations
• references/claude.md - Claude-specific XML patterns
• references/gemini.md - Gemini system instruction patterns
• references/grok.md - Grok conversational patterns
• references/examples.md - Concrete examples and templates

Related Skills

• engineering-discipline - Core principles for sustainable code
• task-decomposition - Breaking down complex tasks
• orchestration - Intelligent tool selection and execution