Cutting-Edge LLMs and Multimodal AI

• Chapter 2.1: Structured Prompting Techniques (Zero/Few-shot, CoT, Tool-Use)

• Chapter 2.2: Retrieval-Augmented Generation (RAG) Overview

• Chapter 2.3: Fine-Tuning vs. Instruction Tuning vs. RLHF

• Chapter 2.4: Evaluation and Safety Alignment Metrics

• Chapter 3.1: Multimodal Transformers (BLIP-2, Flamingo, GPT-4V, Gemini)

• Chapter 3.2: Vision Encoding and Alignment with Text Embeddings

• Chapter 3.3: Image Captioning, Visual Q&A, Scene Understanding

• Chapter 3.4: Visual Prompting, Layout Understanding, Image-to-Text Inference

• Chapter 4.1: Audio-Language Systems (Whisper, AudioCraft, VALL-E)

• Chapter 4.2: Video-Language Interaction (Sora, Pika Labs, RunwayML)

• Chapter 4.3: Code + Text and Structural Models (Code LLMs, ReAct)

• Chapter 4.4: Multimodal Embeddings and Cross-Modal Retrieval

• Chapter 5.1: Multimodal AI in Search, Design, Robotics, and Healthcare

• Chapter 5.2: Tool-Use and API-Augmented Agents (Auto-GPT, OpenAgents, ReAct)

• Chapter 5.3: Agent Simulations, Planning, and Toolchains

• Chapter 5.4: Case Studies: Enterprise LLM Use and Multimodal Integrations

• Chapter 6.1: AI Hallucinations, Safety, and Guardrails

• Chapter 6.2: AI Copyright, Content Authenticity, and Watermarking

• Chapter 6.3: Regulation Trends and Global AI Policies

• Chapter 6.4: What’s Next: Multimodal General Intelligence and Open Challenges

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