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

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+# Common Prompt Engineering Mistakes
+
+Learn from these common pitfalls to create better prompts.
+
+## Mistake 1: Being Too Vague
+
+❌ **Problem:**
+```
+Write something about AI
+```
+
+**Why it fails:**
+- No clear scope or direction
+- Undefined output format
+- Unknown target audience
+- No length specification
+
+✅ **Solution:**
+```
+Write a 500-word introduction to large language models for software developers.
+
+Include:
+- What LLMs are and how they work (high-level)
+- 3 practical use cases in software development
+- 1 code example of API usage
+- Limitations to be aware of
+
+Tone: Technical but accessible
+Format: Markdown with headers
+```
+
+---
+
+## Mistake 2: Instruction Confusion
+
+❌ **Problem:**
+```
+Here is some text about Python. It talks about lists and dictionaries.
+Can you make it better and also add examples? Use clear language.
+```
+
+**Why it fails:**
+- Instructions mixed with content
+- Unclear what "better" means
+- No structural separation
+
+✅ **Solution:**
+```xml
+<instructions>
+Improve the following text by:
+1. Adding 2 code examples (one for lists, one for dictionaries)
+2. Simplifying complex sentences
+3. Adding subheadings for each topic
+</instructions>
+
+<text_to_improve>
+[original text here]
+</text_to_improve>
+
+<target_audience>
+Beginner Python developers
+</target_audience>
+```
+
+---
+
+## Mistake 3: No Examples (When Needed)
+
+❌ **Problem:**
+```
+Extract entities from customer support tickets in a structured format.
+```
+
+**Why it fails:**
+- "Structured format" is ambiguous
+- No clarity on what entities matter
+- Different interpretations possible
+
+✅ **Solution:**
+```xml
+<instructions>
+Extract entities from customer support tickets.
+</instructions>
+
+<examples>
+<example>
+<input>
+My order #12345 never arrived. I contacted support@example.com but no response.
+</input>
+<output>
+{
+  "order_id": "12345",
+  "issue_type": "delivery",
+  "contact_method": "email",
+  "email": "support@example.com"
+}
+</output>
+</example>
+</examples>
+
+<input>
+[new ticket]
+</input>
+```
+
+---
+
+## Mistake 4: Prompt Too Long
+
+❌ **Problem:**
+```
+[5000 words of background information]
+[500 words of instructions]
+[1000 words of examples]
+
+Now answer this simple question: What is the capital of France?
+```
+
+**Why it fails:**
+- Wastes tokens on irrelevant context
+- Dilutes important information
+- Slower and more expensive
+- May confuse the model
+
+✅ **Solution:**
+```
+What is the capital of France?
+```
+
+**Principle:** Use minimum necessary context
+
+---
+
+## Mistake 5: Not Controlling Output Format
+
+❌ **Problem:**
+```
+Give me the user's information from this text.
+```
+
+**Result:** Unpredictable format - might be prose, bullet points, or inconsistent structure
+
+✅ **Solution:**
+```xml
+<instructions>
+Extract user information and return as JSON.
+</instructions>
+
+<input>
+[text here]
+</input>
+
+<output_format>
+{
+  "name": string,
+  "email": string | null,
+  "phone": string | null
+}
+</output_format>