Prompt Engineering

Design effective prompts for LLMs. Apply when crafting skills, agents, output styles, system prompts, or any AI instructions.

Quick Start

Most prompts need only three things:

•Clear task — action verb + objective
•Output format — explicit structure
•One example — if format matters

Start here. Add complexity only when this fails.

code

Summarize the following article in 3 bullet points.
Each bullet should be under 20 words.
Focus on business implications.

Example output:
- Revenue increased 15% due to new product line launch
- Operating costs reduced through automation initiatives
- Market share expanded in European regions

Article:
{{ARTICLE}}

What's Wrong With Your Prompt?

Symptom	Fix	Details
Wrong format	Add explicit format + example	below
Missing information	Be more specific about what to include	below
Hallucination	Add context, request citations	below
Ignores instructions	Move instructions to end, use XML tags	long-context.md
Complex reasoning fails	Add "Think step by step"	reasoning-techniques.md
Inconsistent results	Add 3-5 examples	learning-paradigms.md
Too verbose	Specify word/sentence limits	below
Security concerns	Validate input, filter output	security.md

Deep Dive References

Topic	When to Read
reasoning-techniques.md	Multi-step reasoning, math, logic, CoT variants
learning-paradigms.md	Zero/few-shot design, example selection
workflow-patterns.md	Multi-prompt pipelines, iterative refinement
security.md	Untrusted input, prompt injection defense
optimization-strategies.md	Prompting vs RAG vs fine-tuning
claude-specific.md	Prefilling, extended thinking, system prompts
long-context.md	20K+ token documents
agent-patterns.md	Tool use, ReAct, PAL patterns

Core Techniques

Use these in order — most problems are solved by #1-3.

1. Be Clear and Direct

The golden rule: Show your prompt to a colleague with minimal context. If they're confused, Claude will be too.

Provide Context

Tell Claude:

•What the task results will be used for
•Who the audience is
•What success looks like

Be Specific

Vague	Specific
"Summarize this"	"Summarize in 3 bullets, each under 20 words"
"Make it better"	"Fix grammar errors, reduce word count by 30%"
"Analyze the data"	"Calculate YoY growth, identify top 3 trends"

Output Format

Always specify format explicitly. Show an example if structure matters:

code

Extract the following as JSON:
- Product name
- Price (number only)
- In stock (boolean)

Example output:
{"name": "Widget Pro", "price": 29.99, "in_stock": true}

2. Use Examples (Few-Shot)

3-5 examples typically sufficient. Cover edge cases.

code

Text: "Great service!" → Positive
Text: "Worst purchase ever" → Negative
Text: "It works as expected" → Neutral
Text: "Absolutely love it!" → ?

Tips:

•Order: simple → complex
•Diverse, representative examples
•Wrap in <examples> tags for clarity

See learning-paradigms.md for example selection strategies.

3. Use XML Tags

Separate components for clarity and parseability:

xml

<instructions>
Analyze the contract for risks.
</instructions>

<contract>
{{CONTRACT_TEXT}}
</contract>

<output_format>
List risks in <risks> tags, recommendations in <recommendations>.
</output_format>

Best practices:

•Consistent tag names throughout
•Reference tags in instructions: "Using the contract in <contract>..."
•Nest for hierarchy: <outer><inner>...</inner></outer>

4. Let Claude Think (Chain-of-Thought)

For complex reasoning, ask Claude to show its work:

code

Think through this step by step, then provide your answer.

Or use structured tags:

code

Think through this in <thinking> tags.
Then provide your answer in <answer> tags.

Critical: Claude must output its thinking. Without outputting the thought process, no thinking actually occurs.

See reasoning-techniques.md for CoT variants, Tree-of-Thoughts, and extended thinking.

5. Use Sequential Steps

For multi-step tasks, number the steps:

code

Your task is to anonymize customer feedback.

Instructions:
1. Replace customer names with "CUSTOMER_[ID]"
2. Replace emails with "EMAIL_[ID]@example.com"
3. Redact phone numbers as "PHONE_[ID]"
4. Leave product names intact
5. Output only processed messages, separated by "---"

Numbered steps ensure Claude follows exact sequence.

Choosing a Technique

code

Simple task, clear format     → Zero-shot (just ask)
Need consistent format        → Few-shot (3-5 examples)
Complex reasoning             → Chain-of-Thought
Very complex problem          → Extended Thinking
Multi-step workflow           → Prompt Chaining
Need external information     → ReAct (reasoning + tool use)
Precise calculation           → PAL (generate code)

For advanced patterns, see agent-patterns.md.

Claude-Specific Features

Prefilling

Start Claude's response to control format:

python

messages=[
    {"role": "user", "content": "Extract as JSON: ..."},
    {"role": "assistant", "content": "{"}  # Prefill
]

Claude continues from {, outputting pure JSON without preamble.

System Prompts (Roles)

Use roles for domain expertise:

python

system="You are a senior security auditor specializing in web applications."

Role prompting activates domain-specific knowledge and reasoning patterns.

Extended Thinking

For very complex problems, use extended thinking mode (API parameter) instead of standard CoT. Use high-level guidance, not prescriptive steps.

See claude-specific.md for details.

Quality Checklist

Before finalizing a prompt:

• Task is clear (single action verb)
• Output format is explicit
• Constraints are specific (not vague)
• Examples cover edge cases (if using few-shot)
• Golden rule passed (colleague wouldn't be confused)
• Security considered (if handling untrusted input)