Example:
Cat → [0.34, 0.67, 0.11...]
Dog → [0.32, 0.69, 0.15...]
Car → [0.91, 0.18, 0.76...]
Cat and Dog embeddings are closer than Cat and Car because their meanings are more similar.
Used in: RAG, Semantic Search, Recommendation Systems, Vector Databases
11. Why is Generative AI so Powerful?
Because it combines:
Massive datasets
Powerful GPUs
Transformer architecture
Large-scale pretraining
Cloud computing
Advanced optimization techniques
12. Challenges of Generative AI
Hallucinations
Bias
High inference cost
Privacy concerns
Copyright issues
Prompt injection attacks
Security risks
13. Common Interview Questions
What is Generative AI?
How is it different from Machine Learning?
What is a Foundation Model?
What is an LLM?
How does an LLM generate text?
What is Tokenization?
What are Embeddings?
What are the limitations of Generative AI?
What are the applications of Generative AI?
Why is Generative AI important today?
🎯 Interview Tips
When answering Generative AI fundamentals, follow this structure:
1. Define the concept clearly.
2. Explain how it works.
3. Give a real-world example.
4. Mention practical applications.
5. Discuss advantages and limitations.
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