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