Prompt Chaining
In one line
Prompt chaining is the simplest agent pattern — a sequence of prompts where each step's output feeds the next step's input.
Going deeper
Prompt chaining is the 'do not ask for everything at once — break it into steps' idea. For content production that might mean topic research → outline → draft → tone pass → meta description, with each step's output feeding the next step's prompt.
It looks simple, but it is surprisingly effective. Splitting the work cuts the load any single prompt has to carry, which stabilises quality, and intermediate outputs are easy to inspect by hand or hand off to another tool. Even more advanced patterns like ReAct and planning agents are essentially extensions of prompt chaining.
In marketing automation it is close to a default. Campaign reports, copy variants, keyword clustering — almost all of these get better quality and easier debugging when you chain three to five steps instead of cramming everything into one prompt.
Related terms
ReAct
ReAct (Reasoning + Acting) is the classic agent pattern where an LLM loops through Thought, Action and Observation steps — reasoning out loud and calling tools as it goes.
AI AgentPlanning Agent
A planning agent is an agent that first drafts a multi-step plan for a goal and then executes it step by step, calling tools as needed.
LLMPrompt Engineering
Prompt engineering is the practice of crafting inputs that steer an LLM toward better outputs — a way to dramatically change result quality without retraining the model.
AI AgentAI Agent
An AI agent is an LLM-driven system that takes a goal, plans the steps, calls the tools it needs and runs the task end-to-end with limited human input.
AI AgentTool Use
Tool use is an LLM calling external APIs, calculators or search systems directly to ground its answers — the foundational behaviour of every agent.
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