Getting Started with Perplexity AI Log in and enroll to track lesson completion. By the end of this lesson, students should be able to: The difference between search and question. Traditional search engines reward short keyword phrases ("salary data science 2026"). Perplexity performs best with natural-language questions that provide context – because it is synthesizing and reasoning, not just retrieving links. The three-question framework. Before submitting any Perplexity search, answer these three questions for yourself: Then build your search incorporating all three. Before the framework: "Data science salaries" After the framework: "What are current median salaries for entry-level data scientists in the United States in 2026? I am a recent computer science graduate evaluating job offers. I want to understand what range is reasonable and what factors affect salary at this career stage." The second version produces targeted, relevant, verifiable results – not a generic overview. Using context in your questions. Context that improves Perplexity results: Follow-up questions to deepen research. After Perplexity responds, follow-up questions allow you to: Perplexity maintains context across follow-up questions within the same search session. Lesser-Known Tip: The "as of" anchor. Adding "as of [current year]" or "most recent available data" to your question helps Perplexity prioritize current sources over older content. Log in and enroll to take this lesson quiz.
Lesson 2: Your First Effective Search – The Three-Question Framework
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