MODULE 3 · Practical AI workflows
A research workflow
A research workflow
Use AI to structure questions and summarize findings while keeping sources visible. In this lesson, you will focus on separating discovery from verification. The goal is not to memorize a checklist, but to understand the decision you are making and the trade-off behind it.
A useful way to study this topic is to connect it to something you have already seen. Notice what changes when the context changes: a choice that is sensible for a small experiment can become fragile when a project, team, or audience grows. Good practice comes from making that distinction deliberately.
How to approach it
- Start with the real problem before reaching for a tool or pattern.
- Use separating discovery from verification as a guide, then adjust it to the context in front of you.
- Keep the first version small enough that you can review what changed and why.
A concrete example
Imagine explaining this choice to a teammate tomorrow. A strong explanation names the goal, the constraint, and the result you expect. If you cannot describe those three things clearly, pause and make the problem smaller before adding more complexity.
Key takeaways
KEY TAKEAWAYS
- A research workflow is most useful when it supports a clear outcome.
- separating discovery from verification becomes easier when the context is explicit.
- Small, deliberate steps make it easier to spot mistakes early.
- Apply the idea once in your own work before moving to the next lesson.
Reading material
A research workflow reflection
A short prompt to apply separating discovery from verification to a current project.
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