10 Essential Prompt Engineering Best Practices from Anthropic
10 Essential Prompt Engineering Best Practices
Anthropic’s latest prompting guide reveals exactly how to get the most out of their frontier models like Opus 4.5. If you want to move beyond basic chats and build reliable AI workflows, you need to internalise these 10 best practices:
1. Be Explicit and Direct
Ambiguity is the enemy of performance. Don’t leave room for interpretation. Clearly state your goal, the constraints, and the desired output format.
2. Leverage XML Tags for Structure
This is Claude’s native language. Use tags like <context>, <instruction>, and <example> to separate different parts of your prompt. It dramatically reduces confusion and hallucinations.
3. Assign a Persona (Role Prompting)
“You are a senior Python engineer” works better than “Write some code.” Framing the AI’s role sets the correct tone, vocabulary, and depth of analysis immediately.
4. Let Claude “Think” (Chain of Thought)
For complex reasoning, explicitly ask Claude to “think step-by-step” or use <thinking> tags before outputting the final answer. This forces the model to reason through the problem, reducing logical errors.
5. Prefill the Response
Don’t just ask; lead the AI. Start the AI’s response for it (e.g. “Here is the JSON output:”). This steers the model into the exact format you need.
6. Show, Don’t Just Tell
Providing 2-3 high quality examples of inputs and desired outputs is often more powerful than paragraphs of instructions.
7. Break Down Complex Tasks
Don’t ask for a miracle in one prompt. Decompose massive requests into a sequence of smaller, logical subtasks. This “chaining” approach yields significantly higher accuracy.
8. Optimise Context Placement
Put your large reference documents (context) at the top of the prompt and your specific instructions/questions at the bottom. This prevents the “lost in the middle” phenomenon.
9. Allow for “I Don’t Know”
Reduce hallucinations by explicitly telling the model: “If the answer is not in the provided context, state that you do not know.”
10. Iterate and Refine
Treat prompting as code. If the output isn’t perfect, ask Claude to critique its own work and suggest improvements.
The Takeaway
Prompt engineering isn’t dead; it has evolved into “Prompt Architecture.” We are moving from chatting with bots to programming them with natural language.