Pattern 1: Prompt Chaining

Sequential Document Analysis Pipeline

Prompt chaining in action

What Is This Pattern?

Prompt chaining is like an assembly line for processing information. Instead of asking AI to do everything at once (which often leads to errors or incomplete analysis), you break the work into sequential steps where each step’s output becomes the next step’s input.

The magic is in the chaining: each AI call builds on the previous one’s work, gradually refining raw data into something we can work with.

How It Works

Conceptual Overview

Instead of counting on one single call to the model, we can chain the output of one model into another.

Architecture Diagram

graph LR
    In[Input]:::input --> LLM1[LLM Call 1]:::llm
    LLM1 -->|Output 1| LLM2[LLM Call 2]:::llm
    LLM2 -->|Output 2| LLM3[LLM Call 3]:::llm
    LLM3 -->|Output 3| Out[Final Output]:::output

    classDef input fill:#8B4444,stroke:#6B3333,color:#fff
    classDef llm fill:#4A7C59,stroke:#3A6B49,color:#fff
    classDef output fill:#8B4444,stroke:#6B3333,color:#fff

Prompt Chaining: Sequential Processing Pipeline

Use Cases

This was my first “pattern” and it’s probably the most logical. In my use case:

  • For the 2025 elections in Portugal we had about 200 pages * eight parties’ programs. We wanted to extract all political measures from all parties.
  • We wanted to categorize each measure into a set of predefined categories
  • We wanted to score measures by “newsworthiness”