AI generative

1- Core Principles of Generative Modeling 2- Key Challenges: Mode Collapse, Posterior Collapse, and Evaluation Metrics 3- Historical Evolution from GANs to Diffusion and Transformer-Based Models 4- Self-Attention Mechanisms and Positional Encodings in GPT-Style Models 5- Decoder-Only vs. Encoder–Decoder Architectures 6- Scaling Laws, Mixture-of-Experts (MoE), and Efficient Inference Techniques 7- Forward and Reverse Diffusion Processes with Noise Scheduling 8- Denoising U-Nets and Classifier-Free Guidance 9- Latent Diffusion for Efficient Multimodal Generation 10- Vision-Language Models and Unified Architectures 11- Audio and Video Generation 12- Agentic Architectures for Multimodal Reasoning 13- Retrieval-Augmented Generation (RAG) and Fine-Tuning Methods (LoRA, QLoRA) 14- Reinforcement Learning from Human Feedback and Direct Preference Optimization 15- Test-Time Training and Adaptive Compute 16- Zero-Shot, Few-Shot, and Chain-of-Thought Prompting Techniques 17- Role-Playing, Structured Output Formats (JSON, XML), and Temperature Control 18- Prompt Compression and Iterative Refinement Strategies 19- Tree-of-Thoughts, Graph Prompting, and Self-Consistency Methods 20- Automatic Prompt Optimization and Meta-Prompting 21- Domain-Specific Adaptation 22- Robust Evaluation Frameworks (LLM-as-Judge, G-Eval) and Hallucination Detection 23- Alignment Techniques (Constitutional AI, Red-Teaming) and Bias Mitigation 24- Production Deployment: API Integration, Rate Limiting, and Monitoring Best Practices 25- AI generative