Course
Understanding How Perplexity AI Works
Course focus:
Perplexity
Course Learning Outcomes
By the end of this course, students should be able to:
- Explain the specific Perplexity capability used in this course without provider hype.
- Build a practical workflow using research-grade search, source triangulation, Spaces/Projects, comparison tables, citation review, and decision briefs.
- Produce a reviewable artifact such as claim ledger, vendor comparison, cited research brief, source-quality matrix, monitoring plan.
- Diagnose common failure modes and revise the workflow.
- Verify quality, privacy, rights, and human-approval requirements before use.
After enrolling, students can complete lesson quizzes as they move through the course.
The course final assessment unlocks after all required lessons and lesson quizzes are complete.
Paid Learning Context
Original baseline context: Students learn how Perplexity's search-to-AI synthesis pipeline works, what sources it draws from, why citation presence does not equal claim accuracy, and what the free vs. Pro feature comparison means for how they should use each.
The paid version of this course treats the topic as a production workflow. Students must leave with a usable artifact, a reusable method, and evidence that the result was reviewed rather than blindly accepted.
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