Prompt Chaining: How to Connect Multiple AI Prompts for Complex Tasks

Single prompts work well for simple tasks. But real-world problems (writing a research report, building a marketing strategy, debugging complex code) require multiple steps, each building on the last.

Prompt chaining is the technique of linking AI prompts together so the output of one becomes the input of the next. It transforms AI from a single-response tool into a multi-step workflow engine.

This guide covers the core patterns, real examples, and practical strategies for building prompt chains that handle complex work reliably.

What Is Prompt Chaining?

Prompt chaining breaks a complex task into a sequence of focused prompts, where each prompt handles one specific subtask. The output from each step feeds into the next, creating a pipeline that produces results no single prompt could achieve.

Simple analogy: Imagine asking one person to write, edit, fact-check, and format an article simultaneously versus having four specialists handle each step in sequence. The specialist approach produces better results because each step gets focused attention.

A basic prompt chain:

Each prompt does one thing well. Together, they produce a better article than any single prompt could.

Why Chaining Works Better Than Long Prompts

You might wonder: why not just write one long, detailed prompt that covers everything? Three reasons:

Focus produces quality. When an AI processes a 500-word prompt with 10 requirements, some requirements get less attention. Breaking those into 10 focused prompts ensures each requirement gets the model's full capacity.

Context stays relevant. Long prompts dilute the AI's attention across many competing instructions. Short, focused prompts keep all attention on the current task.

Errors are catchable. If step 3 of a chain produces a weak result, you can rerun just that step. With a monolithic prompt, you restart from scratch.

Research consistently shows that decomposing complex tasks into subtasks improves AI output quality, the same principle behind chain-of-thought prompting applied at the workflow level.

4 Core Chaining Patterns

Pattern 1: Sequential Chain

The simplest and most common pattern. Each prompt runs in order, with the previous output feeding the next input.

When to use: Tasks with a natural step-by-step progression, like research → outline → draft → edit.

Example, Product Launch Email Sequence:

Pattern 2: Parallel Chain

Multiple prompts run independently on different aspects of the same task, then a final prompt combines the results.

When to use: When you need multiple independent analyses or perspectives combined into one deliverable.

Example, Competitive Analysis:

Pattern 3: Conditional Chain

The chain branches based on the output of a classification or evaluation step.

When to use: When different inputs require different treatment, like routing customer queries or categorizing content.

Example, Customer Support Routing:

Pattern 4: Iterative Refinement Chain

The same prompt runs multiple times with feedback from an evaluation step, progressively improving the output.

When to use: Creative tasks where quality improves through iteration, such as writing, design briefs, and strategy documents.

Example, Landing Page Copy:

Building Effective Chains: Best Practices

Start with the End Result

Work backward from your desired final output. What does the finished product look like? Then identify the steps needed to get there.

Keep Each Step Focused

Each prompt in the chain should have exactly one job. If you find a prompt doing two things, split it.

Too broad: "Research competitors and write the analysis"

Focused: "Research competitor pricing" → "Analyze pricing implications"

Include Quality Gates

Add evaluation steps between critical stages. These catch errors before they propagate through the chain.

Pass Context Efficiently

When feeding output from one step to the next, be explicit about what the AI should use and how.

Document Your Chains

Keep a record of prompt chains that work well. Reusable chains save time and produce consistent results.

Real-World Prompt Chain Examples

Content Production Chain

This chain produces a blog post from topic to final draft:

Code Review Chain

This chain systematically reviews code:

Strategic Planning Chain

This chain develops a go-to-market strategy:

Prompt Chaining with Frameworks

Prompt frameworks work especially well as building blocks within chains. Each step in the chain can use a framework structure:

Chain Step 1 (using RACE):

Chain Step 2 (using CO-STAR):

Combining chaining with frameworks gives you both structure (from frameworks) and workflow capability (from chaining).

Common Chaining Mistakes

Chains That Are Too Long

Problem: 10+ step chains where errors compound and context gets lost.

Fix: Keep chains to 3-6 steps. If you need more, group steps into sub-chains.

Not Carrying Context Forward

Problem: Step 4 produces output that ignores decisions made in Step 2.

Fix: Explicitly reference previous outputs: "Based on the [specific element] from the previous analysis..."

Skipping Evaluation Steps

Problem: Errors in early steps propagate through the entire chain undetected.

Fix: Add quality gates after critical steps, especially research, analysis, and drafting.

Over-Engineering Simple Tasks

Problem: Using a 5-step chain for a task a single prompt handles fine.

Fix: Only chain when the task genuinely benefits from decomposition. If one prompt gets you 90% there, don't chain.

Getting Started with Chaining

If you're new to prompt chaining, start with these steps:

• Pick a recurring complex task you currently handle with AI, such as content creation, analysis, or planning
• Break it into 3-4 focused steps: research, draft, review, refine
• Write a prompt for each step using the techniques from our prompt writing guide
• Run the chain manually, pasting outputs from one step into the next
• Evaluate the result compared to your single-prompt approach
• Refine the chain by adjusting step order, adding quality gates, and removing unnecessary steps

As you get comfortable, explore automation tools that run chains programmatically, or use the CRISPE framework with its built-in experimentation component to test different chain configurations.

What's Next

Prompt chaining opens the door to more advanced AI orchestration patterns. Once you're comfortable with basic chains, explore:

• Advanced prompt engineering techniques for stronger individual prompts within chains
• Prompt frameworks to structure each step consistently
• Prompt templates for ready-made building blocks you can chain together
• Multi-agent workflows where different AI personas handle different chain steps

The key principle: complex outputs come from composing simple, focused steps, not from writing longer prompts.