Chain complex prompts for stronger performance

When working with complex tasks, Claude can sometimes drop the ball if you try to handle everything in a single prompt. Chain of thought (CoT) prompting is great, but what if your task has multiple distinct steps that each require in-depth thought?

Enter prompt chaining: breaking down complex tasks into smaller, manageable subtasks.

1. Accuracy: Each subtask gets Claude's full attention, reducing errors.
2. Clarity: Simpler subtasks mean clearer instructions and outputs.
3. Traceability: Easily pinpoint and fix issues in your prompt chain.

Use prompt chaining for multi-step tasks like research synthesis, document analysis, or iterative content creation. When a task involves multiple transformations, citations, or instructions, chaining prevents Claude from dropping or mishandling steps.

Remember: Each link in the chain gets Claude's full attention!

1. Identify subtasks: Break your task into distinct, sequential steps.
2. Structure with XML for clear handoffs: Use XML tags to pass outputs between prompts.
3. Have a single-task goal: Each subtask should have a single, clear objective.
4. Iterate: Refine subtasks based on Claude's performance.

• Multi-step analysis: See the legal and business examples below.
• Content creation pipelines: Research → Outline → Draft → Edit → Format.
• Data processing: Extract → Transform → Analyze → Visualize.
• Decision-making: Gather info → List options → Analyze each → Recommend.
• Verification loops: Generate content → Review → Refine → Re-review.

You can chain prompts to have Claude review its own work! This catches errors and refines outputs, especially for high-stakes tasks.