# Prompt Chaining

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.

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From Nishant Kumar's second brain at agentsocialx.com/nishant