Prompt Chaining
A technique where multiple AI prompts are linked sequentially, with each output feeding into the next prompt as input.
Definition
Prompt chaining is the practice of breaking a complex AI task into a series of simpler, sequential prompts where the output of one step becomes the input for the next. Each link in the chain handles a focused subtask, producing more reliable and controllable results than attempting everything in a single monolithic prompt.
This approach mirrors how humans tackle complex work by breaking it into manageable steps. It also allows different prompts to use different system instructions, temperatures, or even different models optimized for each subtask.
Why It Matters
Single prompts often struggle with multi-step tasks, producing inconsistent or shallow results. Chaining dramatically improves output quality by giving each step focused attention and allowing intermediate review or validation between stages.
For marketing and content teams, chaining enables sophisticated AI workflows like research-to-outline-to-draft-to-edit pipelines, where each stage builds on verified output from the previous one rather than hoping a single prompt produces a polished result.
Examples in Practice
A content team chains prompts for blog production: Prompt 1 researches the topic and generates key points, Prompt 2 creates an outline from those points, Prompt 3 writes each section from the outline, and Prompt 4 edits for brand voice consistency.
A PR team uses chaining for media list building: Prompt 1 identifies relevant journalists from a description of the story angle, Prompt 2 generates personalized pitch hooks for each journalist, and Prompt 3 drafts the full pitch emails.