I’d like to propose adding an AI-Assisted Coding Guide for Perplexity AI, a conversational search engine that combines large language models with cited web results.

Perplexity can be especially useful for SE-EDU students working on iP/tP and team projects because it helps with:

• Quick technical research: Looking up Java/Gradle/JavaFX APIs, dependency updates, or Checkstyle rules with citations.
• Concept explanations: Clarifying UML notations, design patterns, or software engineering principles in a concise way.
• Summarising resources: Condensing long articles, GitHub discussions, or StackOverflow threads into actionable insights.
• Comparisons: Evaluating multiple libraries/tools (e.g., JSON parsing options, logging frameworks).

The guide can also stress responsible usage:

• Always verify Perplexity’s sources, since LLM summaries can still misinterpret details.
• Use it for learning and exploration, not for copy-pasting large blocks of code.
• Treat it as a supplement to official documentation and project guidelines.

By including this guide, students will learn how to use Perplexity AI effectively as a research and productivity tool, while staying aligned with SE project requirements.