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.

Why chain prompts?

  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.

When to chain prompts

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!

Debugging tip: If Claude misses a step or performs poorly, isolate that step in its own prompt. This lets you fine-tune problematic steps without redoing the entire task.

How to chain prompts

  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.

Example chained workflows:

  • 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.
Optimization tip: For tasks with independent subtasks (like analyzing multiple docs), create separate prompts and run them in parallel for speed.

Advanced: Self-correction chains

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