🧠 Generative AI Core Concepts

1. Large Language Models (LLMs)
• Trained on massive text datasets
• Predict next word/token based on context
• Examples: GPT, LLaMA, Claude

2. Tokenization
• Splits text into smaller units (tokens)
• Models process these tokens, not raw text
• E.g., "ChatGPT is smart" → ["Chat", "G", "PT", "is", "smart"]

3. Embeddings
• Turns tokens into numeric vectors
• Captures meaning, similarity, context
• Used for search, clustering, recommendation

4. Attention Mechanism
• Helps models focus on relevant parts of input
• Core of the Transformer architecture
• Improves understanding of long sequences

5. Transformers
• Deep learning models using self-attention
• Backbone of modern generative AI
• Handles parallel processing better than RNNs

6. Prompt Engineering
• Technique to guide model outputs
• Uses carefully designed input text
• Better prompts = better results

7. Temperature & Top-p
• Controls randomness in output
• Lower = focused, higher = creative
• Use temperature 0.7–1.0 for varied results

8. Fine-tuning
• Training a base model on custom data
• Improves performance for specific use cases
• Needs more compute and data

9. RAG (Retrieval-Augmented Generation)
• Combines LLMs with external knowledge
• Retrieves relevant info, feeds it to the model
• Reduces hallucinations

10. Multi-modal Models
• Handle text + images/audio/video
• Example: GPT-4, Gemini, DALL·E
• Powers tools like image captioning and voice chat

💡 Learn these to build real-world GenAI apps faster.

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