Is There a Design Pattern for AI Workflows? Exploring Prompt Chaining

When using large language models, giving a single complex instruction often leads to unstable, ill‑structured answers because the request exceeds the model's optimal processing scope.

The remedy is prompt chaining : decompose a large task into a series of smaller steps, feeding each step's output as the next step's input, much like an assembly line.

What is prompt chaining? It splits a complex task into multiple simple stages. For example, to write an article about "remote work":

Step 1 – List three advantages and two disadvantages of remote work.

Step 2 – Find a real‑world example for each advantage and disadvantage.

Step 3 – Organize the points and examples into a complete article.

Compared with a single‑prompt request ("Write a blog post about remote work with three pros, two cons, and examples"), the stepwise approach avoids logical confusion and improves controllability.

Four advantages of prompt chaining :

More accurate results – each step has a clear goal, reducing errors.

Easier debugging – unsatisfactory output can be traced to a specific step.

High flexibility – steps can be added, removed, or modified to suit different needs.

Handles complex tasks – enables creation of business plans, technical proposals, and other multi‑stage outputs.

Typical application scenarios include report generation (outline → data collection → writing → polishing), intelligent customer service (understand query → retrieve answer → generate reply → adjust tone), and content creation (idea → outline → draft → edit).

Practical n8n tutorial : Using the n8n workflow platform, the article builds an article generator that calls a locally hosted Ollama model qwen3:30b. The workflow consists of three Basic LLM Chain nodes:

Key Point Generator – prompt: "List concise, structured key points and perspectives for the topic {{ $json.chatInput }}".

Outline Generator – prompt: "Create an article outline for '{{ $(\'When chat message received\').item.json.chatInput }}' with clear sections and brief descriptions (Introduction, Body, Conclusion)."

Article Generator – prompt: "Write a high‑quality article on the following topic: '{{ $(\'When chat message received\').item.json.chatInput }}' using the outline below: {{ $json.text }}".

Each node’s configuration is illustrated with screenshots (image URLs retained). After configuring, running the workflow automatically produces key points, an outline, and the final article.

Conclusion – Prompt chaining is a simple yet powerful AI workflow design pattern that improves result accuracy, debuggability, flexibility, and scalability, and can be implemented with workflow tools such as n8n to connect multiple LLM calls.