# Contents

#### 🔍 Foundations of GenAI

* What is Generative AI?
* History of Generative AI
* Difference: Generative vs. Discriminative Models
* Key Concepts: Tokens, Prompts, Context Window
* Prompt Engineering (Basics)

***

#### 🧠 Models & Architectures

* Transformer Architecture (Simplified)
* GPT vs BERT vs T5
* Diffusion Models (for Images)
* Multimodal LLMs (text + image/audio/video)
* Open-Source vs Closed Models

***

#### 🛠️ Model Providers (Expand your existing)

* Mistral
* Cohere
* Meta (LLaMA models)
* xAI (Grok)
* Google DeepMind (Gemini's origin)

***

#### 🧰 Ecosystem Tools & Frameworks

* Prompt Layer
* LlamaIndex (formerly GPT Index)
* Haystack
* RAG (Retrieval-Augmented Generation)
* Guardrails AI (Output validation)
* ReAct and CoT Prompting

***

#### 🧠 Memory & Agents

* Agent Memory (short-term vs long-term)
* Tool Use and Function Calling
* Agentic Workflows (vs Pipelines)
* LangChain Agents vs AutoGen Agents
* State Machines vs Event-Driven Agents

***

#### 📦 Infrastructure & Storage

* Vector DBs: FAISS, Qdrant, Weaviate, Pinecone, Milvus
* Embedding Models: OpenAI, HuggingFace, Cohere, BAAI
* Rerankers & Hybrid Search (BM25 + Embeddings)
* Chunking Strategies for Documents

***

#### 🛡️ Ethics & Limitations

* AI Hallucinations
* Bias in LLMs
* Data Privacy and Security
* Copyright and Content Generation
* Human-in-the-Loop (HITL)

***

#### ⚙️ Use Cases & Applications

* GenAI for Education
* GenAI for LegalTech
* GenAI in Healthcare
* Chatbots vs Knowledge Assistants
* Content Generation (Text, Code, Images)

***

#### 🧪 Evaluation & Testing

* Prompt Testing
* LLM Benchmarks (HELM, MMLU, TruthfulQA)
* Guardrails for Output Control
* Evaluating Relevance, Coherence, Safety

***

#### 📈 Trends & Future

* Synthetic Data Generation
* Fine-Tuning vs PEFT vs LoRA
* Tiny LLMs (for Edge Devices)
* AI Agents + Robotics
* Self-Improving AI Systems


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