AI, Machine Learning and Generative AI Basics

Learning Outcome

Separate everyday AI terms and know what each technology is actually useful for.

Core Ideas

• AI: Software behavior that performs tasks associated with human intelligence.
• Machine learning: Models improving from examples or data patterns.
• Generative AI: Models creating new text, images, code, or other content.
• Rules engine: Explicit if-then logic written by people.

Career Use Case

A fresher comparing AI tools for resume screening can separate rule-based filters, prediction models, and generative writing helpers before choosing a workflow.

Practical Workflow

1. Start by naming the outcome: what should improve after using AI, Machine Learning and Generative AI Basics?
2. Add the input material, constraints, and success criteria before asking for output.
3. Ask for assumptions and uncertainty when the answer affects a real decision.
4. Verify important claims, numbers, and policy statements before publishing or acting.

Hands-On Mini Task

• Write one sentence each for AI, machine learning, generative AI, and a rules engine using a hiring or education example.
• A good answer names what each system can and cannot decide without human review.
• Before moving on, explain how AI and Machine learning change the decision.

Common Mistakes

• Using a generic prompt when the task needs clear context.
• Accepting polished wording as proof of accuracy.
• Sharing private data without redaction or approval.
• Skipping a final human review for important decisions.

Quick Revision

Module 1: AI Foundations lesson 1 is about practical judgement: use AI to increase speed, but keep the goal, context, evidence, and accountability clear.

FAQs