How to Effectively Use AI Tools as a Tech Major

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How to Effectively Use AI Tools as a Tech Major

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BACKUP CODE

EFFECTIVE AI STRATEGY

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

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WHY AI?

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.

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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

But how can we use AI to…

What is Cursor?

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

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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.

Demo Time!

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

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.

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.

Do I HAVE to use Cursor?

Questions?

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