What is prompt chaining?
How Prompt Chaining Works
Instead of asking an AI to do everything at once, you guide it through steps. For example, first ask it to outline an article, then to write each section based on that outline, and finally to edit the full draft. Each prompt builds on the previous response.
This approach works because language models have limited context and can lose track of multiple instructions. By chaining, you keep each step simple and check the output before moving on.
- Step 1: Generate a list of ideas or an outline.
- Step 2: Expand each idea into a paragraph or section.
- Step 3: Review and refine the combined text.
- Step 4: Format the final output as needed.
When to Use Prompt Chaining
Chaining is useful for tasks that require multiple stages, like writing a research report, planning a trip, or debugging code. It also helps when you need to verify facts or logic at each step.
For simpler tasks, a single well-crafted prompt may be enough. Chaining adds overhead, so use it when quality and accuracy matter more than speed.
Common mistakes
- Thinking chaining is always necessary; for simple tasks, a single prompt works fine.
- Not checking intermediate outputs, which can lead to errors propagating through the chain.
- Making each step too vague, so the AI doesn't know what to focus on.
