Prompt Chaining
The Assembly Line
6 min read
The Assembly Line
A car isn't built in one step — it moves through stations, each doing one job perfectly before passing to the next.
Station 1 builds the chassis. Station 2 adds the engine. Station 3 installs electronics. Each station does one thing well. Prompt chaining works the same — break a complex task into sequential prompts, where each output becomes the next input. The result is far better than asking the AI to do everything in one giant prompt.
In Plain English
Prompt chaining is breaking a complex task into a series of smaller prompts, where each AI response feeds into the next prompt. It produces higher quality outputs than one massive prompt and lets you review each step.
The Technical Picture
Prompt chaining implements a sequential pipeline where each LLM call's output is programmatically or manually injected as context into the next call. This reduces token load per call, improves output quality through focused subtasks, and enables human review at each checkpoint.
Real-World Examples
- Step 1: Research competitors → Step 2: Identify gaps → Step 3: Write positioning statement
- Step 1: Extract key facts from document → Step 2: Identify inconsistencies → Step 3: Write summary
- Lovable's sequential build prompts are a form of prompt chaining
Complex tasks deserve multiple focused prompts — chain them, don't cram them.