Chapter 1: Prompt Chaining

Break down complex tasks into sequential, manageable steps where each output feeds into the next prompt

Beginner15 min readInteractive Playground

Core PatternBeginner Friendly

Prompt Chaining Pattern

Master the art of breaking complex tasks into sequential steps, where each step's output becomes the next step's input - like a well-orchestrated assembly line for AI.

Prompt Chaining Diagram

Topic: prompt-chaining

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What is Prompt Chaining?

Prompt chaining is a powerful technique that breaks down complex tasks into smaller, manageable steps. Instead of asking an LLM to solve everything at once, you create a sequence where each step's output becomes the next step's input.

Think of it like cooking: You don't throw all ingredients together at once. You prep vegetables, cook protein, make sauce, then combine everything. Each step builds on the previous one!

Limitations of Single Prompts

While single prompts are convenient, they have significant limitations when dealing with complex tasks:

Context Overload

Cramming too much information into one prompt confuses the model and reduces output quality

Lack of Focus

The model tries to handle multiple objectives simultaneously, leading to suboptimal results

Difficult Debugging

When something goes wrong, it's hard to identify which part of the complex prompt failed

No Intermediate Validation

You can't verify or correct intermediate steps before proceeding to the final output

Why Use Prompt Chaining?

Better Accuracy

Each step focuses on one specific task, reducing errors and improving output quality

Easier Debugging

When something goes wrong, you can pinpoint exactly which step failed

Modular Design

Reuse individual steps across different workflows and applications

Context Control

Maintain focus by providing only relevant information at each step

Context Engineering vs Prompt Engineering

Understanding the distinction between these two concepts is crucial for building effective AI systems:

Prompt Engineering

•Focuses on crafting the instructions given to the model
•Optimizes how you ask the question
•Uses techniques like few-shot examples, role prompting, and structured formats
•Typically operates within a single prompt

Context Engineering

•Focuses on providing the right information at the right time
•Optimizes what information the model receives
•Uses techniques like RAG, memory management, and dynamic context selection
•Operates across multiple interactions and steps

Key Insight: Prompt chaining combines both approaches - using prompt engineering for each individual step while employing context engineering to manage information flow between steps.

How Prompt Chaining Works

Break Down the Task

Identify the distinct sub-tasks needed to complete your goal

Design Focused Prompts

Create a specific prompt for each sub-task with clear instructions

Execute Sequentially

Run each prompt in order, passing outputs to the next step

Combine Results

The final step produces your complete, refined output

6 Practical Applications

Here are six real-world scenarios where prompt chaining delivers exceptional results:

Content Generation

Generate blog posts, articles, or marketing copy by chaining topic ideation → outline creation → content writing → SEO optimization

Data Analysis

Analyze datasets by chaining data cleaning → statistical analysis → insight extraction → visualization recommendations

Customer Support

Handle support tickets by chaining issue extraction → categorization → knowledge base search → response generation

Code Review

Review code by chaining syntax analysis → best practices check → security scan → improvement suggestions

Research Synthesis

Synthesize research by chaining paper summarization → key findings extraction → cross-paper analysis → comprehensive report

Product Development

Develop products by chaining market research → feature ideation → technical feasibility → roadmap creation

Beginner Example: Blog Post Generator

Let's create a simple blog post generator using prompt chaining:

Step 1: Generate Topic Ideas

Prompt: "Generate 5 blog post ideas about sustainable living"

Output: List of 5 topics

Step 2: Create Outline

Prompt: "Create outline for: [Selected Topic]"

Output: Structured outline with sections

Step 3: Write Content

Prompt: "Write 500-word blog post from outline:\\n[Outline]"

Output: Complete blog post

Advanced: Structured Output & Validation

For production systems, use structured outputs (JSON/XML) to ensure reliable data passing between steps:

Example: Market Research Chain with JSON Output

{
  "trends": [
    {
      "trend_name": "AI-Powered Personalization",
      "supporting_data": "73% of consumers prefer brands that personalize",
      "growth_rate": "28%",
      "confidence_score": 0.92
    },
    {
      "trend_name": "Sustainable Products",
      "supporting_data": "ESG products grew 28% vs 20% for others",
      "growth_rate": "28%",
      "confidence_score": 0.88
    }
  ],
  "metadata": {
    "sources_analyzed": 15,
    "date_range": "2023-2024",
    "reliability": "high"
  }
}

Benefits of Structured Output:

Type Safety

Validate data types and structure before passing to next step, catching errors early

Easy Parsing

Programmatically extract specific fields without complex string parsing

Schema Validation

Use JSON Schema or Pydantic to enforce required fields and data formats

Debugging

Inspect intermediate outputs easily to identify where chains break

Pro Tip: Always validate structured outputs before passing them to the next step. Use libraries like Pydantic (Python) or Zod (TypeScript) to define schemas and catch errors early.

Visual Summary

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Sequential Processing

Break complex tasks into ordered steps

📊

Structured Data Flow

Use JSON/XML for reliable information passing

✅

Validation at Each Step

Verify outputs before proceeding to next step

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Modular & Reusable

Individual steps can be reused across workflows

Key Takeaways

Prompt chaining breaks complex tasks into sequential, focused steps
Each step's output becomes the input for the next step in the chain
Use structured outputs (JSON) for reliable data passing between steps
Context engineering manages information flow while prompt engineering optimizes instructions
Single prompts suffer from context overload and lack of intermediate validation
Frameworks like LangChain and LangGraph simplify chain implementation
This pattern is foundational for building sophisticated AI agents