Perplexity AI: Comprehensive Guide to Generative AI Course

Lesson 1 • Free vs. Pro Account Features

Compares capability tiers across free and Pro plans, covering model access, usage limits, and exclusive tools. Helps students choose the right plan for their goals.

Lesson 2 • Understanding AI Models Inside Perplexity

Introduces the underlying language models available within Perplexity and explains when each is appropriate. Connects model selection to output quality outcomes.

Lesson 3 • Platform Overview and Interface

Maps every major UI element, navigation panel, and settings menu. Students gain confident, independent navigation before attempting any advanced features.

Lesson 1 • Follow-Up Questions and Conversation Flow

Teaches iterative querying within a thread to refine, expand, or redirect answers. Builds the conversational research skill central to Perplexity's workflow.

Lesson 2 • Search Focus Modes Explained

Covers all available focus modes—Web, Academic, Writing, Video, Social, and more—and their optimal use cases. Students match mode to task for faster, more relevant results.

Lesson 3 • Anatomy of an Effective Query

Breaks down query components—context, intent, constraints, and format cues—and shows how each affects output. Directly improves result quality from the first interaction.

Lesson 4 • Evaluating and Verifying AI Answers

Trains critical assessment of Perplexity outputs using citations, source quality, and cross-referencing. Ensures students never accept AI answers uncritically.

Lesson 1 • Zero-Shot and Few-Shot Prompting

Contrasts direct instruction prompts with example-driven prompts and shows when each outperforms the other. Students build a practical toolkit of prompting patterns.

Lesson 2 • Prompt Templates for Professional Tasks

Provides reusable prompt templates for research, writing, summarization, and data extraction. Students leave with a ready-to-deploy personal prompt library.

Lesson 3 • Prompt Engineering Fundamentals

Introduces core prompting concepts—role assignment, instruction clarity, and output specification. Provides the theoretical framework applied throughout the chapter.

Lesson 4 • Diagnosing and Fixing Poor Outputs

Develops a systematic debugging process for vague, incorrect, or off-format AI responses. Closes the loop between prompt design and output quality.

Lesson 1 • Competitive and Market Research

Uses Perplexity to gather, compare, and synthesize competitive intelligence and market data. Translates research skills into direct business value.

Lesson 2 • Academic and Scientific Research Use Cases

Applies Perplexity's Academic focus mode to literature reviews, citation discovery, and hypothesis exploration. Demonstrates research-grade capability for scholarly work.

Lesson 3 • Multi-Source Synthesis Techniques

Teaches how to aggregate, compare, and synthesize information from multiple Perplexity responses. Produces coherent, well-supported research outputs from diverse sources.

Lesson 4 • Using Collections to Organize Research

Demonstrates creating, naming, and managing Collections to store and retrieve research threads. Integrates organization directly into the research workflow.

Lesson 1 • Drafting Documents and Reports

Covers prompting strategies for generating structured first drafts of reports, memos, and proposals. Connects writing assistance to real professional deliverables.

Lesson 2 • Summarization at Scale

Demonstrates summarizing long documents, articles, and reports using targeted prompts. Saves significant time on information-dense professional reading tasks.

Lesson 3 • Writing for Different Audiences and Formats

Adapts content for technical, executive, and general audiences using format-specific prompts. Builds versatile writing capability across professional contexts.

Lesson 4 • Editing, Rewriting, and Improving Text

Teaches prompts for grammar correction, clarity improvement, tone adjustment, and conciseness. Turns Perplexity into a real-time editorial assistant.

Lesson 1 • Building Analytical Frameworks with AI

Demonstrates using Perplexity to construct and populate analytical frameworks like SWOT, PESTLE, and gap analysis. Accelerates structured thinking for complex problems.

Lesson 2 • Extracting Structured Data from Text

Teaches prompts that convert unstructured text into tables, lists, and structured formats. Bridges AI output and downstream data workflows.

Lesson 3 • Quantitative Analysis Assistance

Covers using Perplexity to interpret statistics, explain formulas, and contextualize numerical data. Supports professionals who work with data but are not data scientists.

Lesson 1 • Perplexity API Fundamentals

Introduces the Perplexity API, authentication, and basic request structure for developers and power users. Enables programmatic integration of Perplexity into external tools.

Lesson 2 • Integrating Perplexity into Workflows

Demonstrates connecting Perplexity to productivity tools, automation platforms, and custom pipelines. Transforms Perplexity from a standalone tool into an embedded workflow component.

Lesson 3 • Image Generation and Visual Outputs

Teaches prompting for image generation within Perplexity and evaluating visual outputs for professional use. Adds a visual dimension to AI-assisted content creation.

Lesson 4 • Customizing Perplexity with System Prompts

Covers setting persistent instructions and personas via system-level prompts for consistent behavior. Enables tailored AI experiences for recurring professional use cases.

Lesson 1 • Building an AI Use Policy

Guides creation of team or organizational policies governing acceptable AI use, attribution, and quality control. Translates ethical principles into actionable governance.

Lesson 2 • Staying Current with AI Developments

Provides strategies for monitoring Perplexity updates, AI research, and industry trends to maintain expertise. Ensures students remain effective as the technology evolves.

Lesson 3 • Privacy, Data Security, and Compliance

Covers data handling practices, privacy settings, and compliance considerations when using AI tools professionally. Protects individuals and organizations from data-related risks.

Lesson 4 • Measuring AI Impact and ROI

Introduces frameworks for measuring time savings, quality improvements, and business impact from AI adoption. Enables data-driven justification of AI investment.