Large language models (LLMs) are incredibly powerful, but anyone who has worked with them extensively knows they can be unpredictable—hallucinating, skipping steps, or misinterpreting instructions. A single monolithic prompt often fails to capture the complexity of real-world tasks.

This is where prompt chaining comes in. Instead of attempting to handle everything at once, prompt chaining breaks a complex process into sequential, structured steps, improving accuracy, traceability, and reliability.

Why Prompt Chaining?

LLMs have a fundamental limitation: they process prompts holistically in a single pass. When a task requires multiple logical steps, forcing everything into a single prompt often leads to dropped details, where the model focuses on some aspects while ignoring others; messy outputs, resulting in jumbled and unstructured information; and difficult debugging, making it hard to pinpoint errors.

By chaining prompts, each step maintains a dedicated focus, keeping outputs clean and structured. It can also be verified and adjusted independently, making debugging more manageable. Additionally, chaining allows for structured data to be passed between steps, improving integration with other tools.

How It Works: A Legal AI Example

Take legal document analysis—a single-step prompt like "Analyze this contract, identify risks, and draft an email to the vendor with proposed changes." sounds reasonable, but in practice, it often fails. The model might mix up risk analysis with contract clauses, or draft a vague, non-actionable email.

Instead, we chain prompts into three distinct steps:

1. Extract contract risks – The first LLM call analyzes risks (e.g., data privacy concerns, liability issues).
2. Generate a structured email – The second prompt uses the extracted risks to create a concise, professional message.
3. Refine for clarity and tone – A final review ensures the email is legally sound and well-structured.

This step-by-step approach produces significantly more reliable results and makes debugging trivial—if the email draft is weak, we refine step 2 without needing to rethink the entire process.

Where This Applies Beyond Legal AI

Prompt chaining is a general framework that applies to various AI-driven workflows:

Content Pipelines → Research → Outline → Draft → Edit
Data Processing → Extract → Transform → Analyze → Summarize
Decision-Making → Gather insights → Rank options → Generate recommendations
Agents & Tool Use → Plan → Execute → Verify → Adjust

Anthropic’s Claude documentation has some solid examples of structured prompt engineering, but we’ve also built a practical guide using Metorial’s agent-based workflow builder, where you can visually construct and test chained prompts.

Want to See It in Action?

We've put together a hands-on guide for building prompt-chaining workflows.

This guide walks through step-by-step implementation—turning theoretical best practices into something you can actually run.

Prompt chaining is the difference between an AI that “sort of” works and one that delivers predictable, high-quality outputs. Give it a try and let us know what you build!