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AI, and YOU!

How to Effectively Use AI Tools as a Tech Major

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AI, and YOU!

How to Effectively Use AI Tools as a Tech Major

Sign In Here!

BACKUP CODE

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EFFECTIVE AI STRATEGY

  • Tool Overview
  • Cursor Overview
  • Basic Prompt Engineering
  • Cursor Interactive Demo + Strategy
  • Other Strategies

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WHY AI?

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GitHub Copilot

Functionality:

  • AI-powered code completion tool integrated into IDEs.
  • Provides context-aware code suggestions and auto-completions.

Benefits:

  • Increases coding efficiency and productivity.
  • Supports multiple programming languages and IDEs like VS Code and JetBrains.

Figstack

Functionality:

  • AI tool for code explanation and translation between languages.
  • Optimizes code efficiency using Big O notation.

Benefits:

  • Helps developers understand and improve code across multiple languages.
  • Provides detailed docstring generation for better code documentation.

Adobe Sensei

Functionality:

  • AI and machine learning framework integrated into Adobe Creative Cloud.
  • Automates repetitive design tasks and enhances creative workflows.

Benefits:

  • Speeds up design processes with features like auto-tagging and smart cropping.
  • Improves user experience by providing data-driven insights and design suggestions

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But how can we use AI to…

Write code?

Find flags?

Automate my job?

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What is Cursor?

  • A fork of Visual Studio Code with integrated AI capabilities.
  • Designed to enhance coding efficiency and productivity.

Key Features:

    • AI Chat with Codebase:
      • Allows interaction with your codebase for questions and insights.
      • Provides context-aware responses by referencing files and documentation.
    • Code Generation and Refactoring:
      • Generate new code or refactor existing code with simple commands.
      • Supports multi-file code generation.
    • Debugging and Lint Fixing:
      • Automated debugging feature to identify and fix issues.
      • Simplifies fixing lint errors with AI assistance.

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Demo Time!

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Basic Prompt Engineering

  • When talking to an AI language model, we need to be deliberate with the instructions we give.
  • In other words, we need to carefully prompt our model to do a certain thing!
  • We can break down a basic prompt into three separate parts - or roles – that gives the language model the necessary information to carry out your request.
    • System Role
    • User Role
    • Task Role

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System Role

  • Definition: This defines the role or capabilities of the language model (LLM). It sets the context for how the model should respond.
  • Importance: By specifying the system, you guide the model’s tone, focus, and depth of knowledge. This helps tailor responses to your specific needs.
  • Utilization: Clearly state what kind of assistant the LLM should be. For example, “You are a language model specialized in educational content” informs the model to prioritize educational outputs.

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User Role

  • Definition: This describes who is asking the question or making the request. It helps the model understand the perspective and needs of the requester.
  • Importance: Knowing the user’s background and intentions helps the model provide more relevant and contextually appropriate responses.
  • Utilization: Include details about yourself or the audience, such as, “I am a college instructor looking to engage tech majors.” This helps the LLM customize its advice.

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Task Role

  • Definition: This specifies the actual request or action you want the model to perform. It clarifies what you need from the model.
  • Importance: A well-defined task ensures the model delivers focused and useful outputs, minimizing ambiguity.
  • Utilization: Clearly articulate what you want, such as “Help me create a lesson plan on AI tools.” This sets a clear objective for the model’s response.

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Do I HAVE to use Cursor?

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Questions?

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