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skills/anthropic-prompt-engineer/references/core_techniques.md

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+# 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:**
+```xml
+<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]