How to design a prompt sequence with clear inputs, outputs, and test steps to handle complex tasks.
Prompt chaining is a method of using the results of the previous step as input for the next step. Instead of asking the AI to research, strategize, write, and test all at once, you break the work down into observable stages.
Four-step sequence
Step 1: Collect and standardize data. Step 2: Analyze according to criteria. Step 3: Create a draft. Step 4: Use a separate prompt to review and correct errors. Each step should have a consistent output format.
Example of building a tutorial.
First, the AI is asked to create a questionnaire that readers frequently ask. Then, the questions are grouped according to the search intent. Next, an outline is created from the selected groups. Finally, each section is written, and the answers are checked to ensure they closely follow the source data.
Quality control points
After each step, check three things: whether the data has been altered, whether the output is correctly structured, and whether the next step has enough information to work with. If a step is wrong, fix it in place instead of rerunning the entire sequence.
When should you not use chaining?
For simple questions or short creative tasks, multiple steps only slow down the process. Chaining is suitable when the work involves risk, multiple facts, or requires verifiable results.
