Sequential thinking breaks a complex task into ordered logical steps, making each action or decision explicit. In prompt work, it helps an AI clarify the task, identify missing context, and refine the wording toward a more useful result. section 9.3 section 9.5.1
A practical sequential prompt typically asks the model to:
- Break the task into logical steps and identify the actions or decisions required at each step. section 9.3
- Check contextual details, expose missing information, and ask clarifying questions about audience, tone, or constraints. section 9.3
- Evaluate and revise its approach repeatedly so the final prompt becomes clearer, more precise, and more adaptable. section 9.3 section 9.5.1
Diagnosing a Python application in Docker
A sequential prompt can turn an unclear debugging request into a checklist: ask whether the container is running with docker ps -a; inspect failures with docker logs if it stopped; examine internal application logs if it is running; verify the Python version and dependencies; then inspect the Dockerfile for configuration problems such as its base image. Each question narrows the next diagnostic action instead of requesting an unexplained final answer. section 9.5.1
Figure 9.6 A chain-of-thought prompt gets the model to reason out a problem like a human would. You break down the problem and experiment with answers until you get the results you desire.
Use the figure to see how a chain-of-thought prompt decomposes a problem and experiments with possible answers. section 9.5.1
