Instead of relying only on what an LLM learned during training, RAG retrieves relevant information from an external knowledge source and provides it as context to the model.

User Question

↓

Retrieve Relevant Data

↓

Provide Context to LLM

↓

Generate Answer

📌 14. What are AI Agents?

AI agents are systems that can reason, plan, use tools, and take actions to accomplish a goal.

For example, an AI agent could:

Understand Goal

↓

Plan Steps

↓

Use Tools

↓

Execute Actions

↓

Evaluate Result

📌 15. What is Generative AI?

Generative AI creates new content based on learned patterns.

It can generate:

• Text

• Images

• Audio

• Video

• Code

📌 16. What are AI Hallucinations?

An AI hallucination occurs when an AI system generates information that appears plausible but is incorrect, unsupported, or fabricated.

This is why AI outputs should be verified, especially for important decisions.

📌 17. What is AI Bias?

AI bias occurs when an AI system produces systematically unfair or skewed results.

Bias can come from:

Training data

Data collection

Feature selection

Model design

Human decisions

📌 18. What is Explainable AI?

Explainable AI (XAI) focuses on making AI decisions understandable to humans.

This is especially important in areas such as:

• Banking

• Healthcare

• Insurance

• Hiring

• Government

📌 19. What is MLOps?

MLOps applies engineering and operational practices to the Machine Learning lifecycle.

It covers:

Model development

Deployment

Versioning

Monitoring

Retraining

Governance

📌 20. What is Responsible AI?

Responsible AI means developing and using AI in a way that considers:

• Fairness

• Privacy

• Security

• Transparency

• Accountability

• Safety

• Human oversight

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