Prompt Chaining: Breaking Complex Tasks into Steps

Prompt chaining is the technique of decomposing a complex task into a sequence of simpler prompts, where the output of each step feeds into the next. It is the AI equivalent of the Unix philosophy: do one thing well, then pipe the result to the next tool. Prompt chaining consistently produces more reliable, higher-quality results than trying to accomplish everything in a single monolithic prompt.

Why Chaining Beats Monolithic Prompts

A single prompt that tries to do everything at once faces several challenges. It competes for attention across multiple objectives, making it hard for the model to prioritize. It consumes context window space with instructions that are only relevant to one part of the task. And when something goes wrong, it is difficult to identify which part of the process failed.

Chaining solves these problems by giving each step a focused objective, clear input, and defined output format. Each link in the chain can be independently tested, debugged, and optimized. This modular approach mirrors best practices in software engineering and makes AI workflows significantly more maintainable.

"Prompt chaining transforms a fragile, all-or-nothing prompt into a robust, debuggable pipeline. Each link is simple enough to work reliably, and the chain as a whole can accomplish remarkably complex tasks."

Chaining Patterns

Sequential Chain

The most common pattern: output from step N becomes input for step N+1. Each step transforms, enriches, or refines the data as it flows through the chain.

Step 1: Extract key facts from the document
Step 2: Organize facts into a logical outline
Step 3: Write a draft based on the outline
Step 4: Edit the draft for clarity and concision
Step 5: Format the final output

Parallel Chain

Multiple prompts run simultaneously on the same input, and their outputs are combined in a final aggregation step. This is useful when you need multiple perspectives or analyses of the same data.

Conditional Chain

The output of one step determines which prompt runs next. This creates branching workflows that can handle different scenarios dynamically, similar to if-else logic in programming.

Loop Chain

A chain that repeats a step until a quality criterion is met. For example, generate content, evaluate it against criteria, and if it does not pass, regenerate with feedback from the evaluation step. This creates a self-improving loop that converges on high-quality output.

Designing Effective Chains

The art of prompt chaining lies in deciding where to break the task apart and how to define the interfaces between steps:

1. Identify natural breakpoints: Look for points in the task where the nature of the work changes, such as from analysis to generation, or from generation to evaluation.
2. Define clear interfaces: Each step should have a well-defined input format and output format. This makes the chain reliable and easy to debug.
3. Keep steps focused: Each prompt should do one thing. If a step is trying to do two things, split it into two steps.
4. Include validation steps: Add intermediate checks that verify the quality of each step's output before it enters the next step.
5. Handle failures gracefully: Design fallback behaviors for when a step produces unexpected output.

Real-World Chaining Examples

Content Creation Pipeline

A blog post creation chain might include: research topic and extract key points, generate an outline from the key points, write each section based on the outline, add examples and quotes, edit for tone and style, and generate SEO metadata. Each step produces a clear artifact that the next step consumes.

Code Review Pipeline

An automated code review chain could include: parse the code and identify functions, analyze each function for bugs, evaluate code style compliance, assess security vulnerabilities, compile all findings into a structured review report. Running these as separate focused prompts produces more thorough reviews than a single "review this code" prompt.

Tools for Prompt Chaining

Several frameworks make prompt chaining easier to implement and manage:

• LangChain: The most popular framework for building LLM chains, with built-in support for sequential, parallel, and conditional chains.
• LlamaIndex: Focuses on data-aware chains that integrate with knowledge bases and data sources.
• Semantic Kernel: Microsoft's SDK for orchestrating AI plugins and chains in enterprise applications.
• Custom Python scripts: For simple chains, a straightforward Python script that makes API calls in sequence is often the most maintainable solution.