I use prompt chaining, also called the Pipeline pattern, to break complex tasks into a series of manageable sub‑problems, letting each LLM call focus on a single step and pass its output to the next.
Prompt chaining, sometimes referred to as Pipeline pattern, represents a powerful paradigm for handling intricate tasks when leveraging large language models (LLMs).
The core idea is to break down the original, daunting problem into a sequence of smaller, more manageable sub‑problems.
The output of one step acting as the input for the next is crucial.
- Divide‑and‑conquer: split tasks into sequential sub‑tasks.
I follow a three‑step workflow: summarisation, trend identification (output as JSON or table), and email composition, which illustrates how structured, role‑focused prompts enable reliable agentic pipelines.
- Modularity & Debuggability: craft, optimise, and inspect each step separately.
- Dependency Chain: pass outputs forward to preserve context.
- External Integration: invoke APIs or databases at any step.
- Role Assignment: give each prompt a clear role (e.g., “Market Analyst”).
- Structured Output: require JSON or XML to avoid ambiguity.