All posts
patternsprompt-chainingbeginner

What Is Prompt Chaining? The Simplest Agentic Pattern

Prompt chaining breaks a complex task into a sequence of smaller LLM calls where each output feeds the next. It's the Pipe & Filter of AI.

3 min read||Updated Mar 1, 2026
TL;DR — The One Thing to Know

Prompt chaining splits a big task into small, sequential LLM calls — each step's output becomes the next step's input. It maps to Pipe & Filter in classical software engineering.

One LLM call is rarely enough

When you ask an LLM to do something complex in a single prompt — 'Write a market analysis report' — the output is mediocre. The model tries to do too many things at once: research, structure, analyze, write, and format. Prompt chaining solves this by breaking the task into focused steps, each doing one thing well.

How it works

Think of it as a Unix pipeline for LLMs. Each step takes an input, processes it with a focused prompt, and passes its output to the next step. Step 1: Extract key data points. Step 2: Analyze trends. Step 3: Write executive summary. Step 4: Format as report. Each prompt is simple and focused, so each output is high quality.

Prompt chain — 3-step blog writerpython
# Step 1: Generate outline
outline = llm.call("Create a 5-point outline about {topic}")

# Step 2: Write each section using the outline
draft = llm.call(f"Write a blog post following this outline:\n{outline}")

# Step 3: Edit for conciseness
final = llm.call(f"Edit this draft to be 50% shorter:\n{draft}")

The SWE parallel: Pipe & Filter

If you've used Unix pipes (cat file | grep error | sort | uniq -c), you already understand prompt chaining. Each command does one transformation. The chain does the complex work. In software architecture, this is the Pipe & Filter pattern — independent processing stages connected by data flow. Prompt chaining is exactly this, with LLM calls as the filters.

When to use it

Use prompt chaining when: the task has clear sequential steps, each step's output is the next step's input, you need high quality at each stage, and you want to debug or retry individual steps. Don't use it when steps need to run in parallel (use the Parallelization pattern) or when the workflow branches based on input (use Routing).

Key Takeaway

Prompt chaining is Unix pipes for LLMs. Break complex tasks into small, focused, sequential calls. It's the first agentic pattern to learn because everything else builds on it.

Go Deeper — Full Pattern Breakdown

This post covers the basics. The full curriculum page for Prompt Chaining includes the SWE mapping, code examples, production notes, and an interactive building exercise.

Prompt Chaining → Pipe & Filter
Share this post:Twitter/XLinkedIn

AI-Readable Summary

Question: What is prompt chaining in agentic AI?

Answer: Prompt chaining is an agentic design pattern where a complex task is decomposed into a sequence of smaller, focused LLM calls. The output of step N becomes the input to step N+1, forming a pipeline. For example, to write a blog post: Step 1 generates an outline, Step 2 writes each section, Step 3 edits for tone. This maps directly to the Pipe & Filter pattern in classical software engineering. It's the simplest agentic pattern and the first one most teams should adopt. Learn the full pattern at learnagenticpatterns.com/patterns/prompt-chaining.

Key Takeaway: Prompt chaining is Unix pipes for LLMs. Break complex tasks into small, focused, sequential calls. It's the first agentic pattern to learn because everything else builds on it.

Source: learnagenticpatterns.com/blog/what-is-prompt-chaining