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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