Generative AI

1. Foundations of Generative AI

• The Paradigm Shift: From Discriminative to Generative Models (understanding probability distributions and data generation).
• The Generative AI Landscape: Foundation models, open-source vs. proprietary, and the ecosystem.
• Tokenization and Embeddings Deep Dive: BPE (Byte-Pair Encoding), WordPiece, and how models actually “see” text and tokens.
• Scaling Laws: Chinchilla scaling laws, compute budgets, and why bigger isn’t always blindly better.

2. Advanced Transformer Architectures & LLM Mechanics

• Beyond Vanilla Transformers: FlashAttention, RoPE (Rotary Position Embeddings), and handling massive context windows.
• Autoregressive Generation: Decoding strategies (temperature, top-k, top-p/nucleus sampling, beam search) and how text is actually sampled token-by-token.
• Mixture of Experts (MoE): Routing, sparse activation, and why modern frontier models use MoE.
• State Space Models (SSMs) & Mamba: Understanding architectures challenging the Transformer monopoly on sequence modeling.

3. Retrieval-Augmented Generation (RAG) Architecture

• The RAG Triad: When to use RAG vs. Fine-tuning.
• Advanced Document Parsing & Chunking Strategies: Semantic chunking, hierarchical chunking, and handling multi-modal documents.
• Vector Databases & Embeddings: Cosine similarity, HNSW indexing, sparse vs. dense retrievers, and hybrid search.
• RAG Evaluation & Optimization: Ragas framework, context recall, precision, and re-ranking (Cohere Re-rank, cross-encoders).

4. Agentic Workflows & Multi-Agent Systems

• The Anatomy of an AI Agent: Reasoning loops (ReAct pattern: Reason, Act, Observe).
• Tool Use & Function Calling: Giving LLMs APIs, calculators, and database access.
• Frameworks in Practice: Building with LangChain, LlamaIndex, and CrewAI/AutoGen.
• Multi-Agent Orchestration: Hierarchical agent teams, memory management, and deadlock prevention.

5. Fine-Tuning & Model Alignment

• Parameter-Efficient Fine-Tuning (PEFT): LoRA, QLoRA, adapters, and why we rarely full-finetune anymore.
• Supervised Fine-Tuning (SFT): Formatting instruction datasets, formatting templates, and hyperparameter tuning for open-weights models.
• Alignment Techniques: RLHF (Reinforcement Learning from Human Feedback), DPO (Direct Preference Optimization), and ORPO.
• Quantization: GPTQ, AWQ, and GGUF formats for running models locally.

6. Generative Vision and Multi-Modal Models

• Vision-Language Models (VLMs): How models like GPT-4V or Llama-3 Vision process images alongside text (CLIP, projector layers).
• Diffusion Models Fundamentals: Forward diffusion, reverse denoising, and score-based generative modeling.
• Advanced Image Generation: Stable Diffusion (1.5 to XL/3), ControlNet for precise spatial control, and latent consistency models.
• Video and Audio Generation: Sora-style video architectures and voice cloning/synthesis (ElevenLabs mechanics).

7. Model Context Protocol (MCP) & Ecosystem Integration

• Introduction to MCP: Why standardizing context and data sources matters for AI clients.
• Building Custom MCP Servers: Exposing local file systems, databases, and APIs to LLM clients.
• Secure Client-Server Communication: Managing prompts, tools, and resources securely.
• Real-world Enterprise Use Cases: Integrating MCP into developer workflows and personal productivity tools.

8. LLMOps: Deployment, Monitoring, and Scaling

• Inference Optimization: vLLM, TensorRT-LLM, continuous batching, andPagedAttention.
• Local and Edge Deployment: Running models with Ollama, llama.cpp, and optimizing for constrained hardware.
• LLM Guardrails & Security: Mitigating prompt injection, jailbreaking, PII leakage, and output hallucination.
• Observability & Tracing: Monitoring token latency, cost tracking, and debugging agent traces with Langsmith or Phoenix.

9. Ethical, Legal, and Societal Impact of GenAI

• Copyright and Intellectual Property: Training data debates, fair use, and generated content ownership.
• Deepfakes, Disinformation, and Synthetic Media: Watermarking standards, provenance, and detection mechanisms.
• Environmental Impact: Carbon footprints of training and serving massive frontier models.
• Bias and Cultural Alignment: Mitigating localized harms and global representation issues in foundational data.

10. Building and Monetizing Micro-SaaS with GenAI

• Identifying Viable Micro-SaaS Niches: Finding repeatable workflows that benefit from LLM wrappers or custom RAG.
• Full-Stack Architecture: Connecting Python backends (FastAPI), vector stores, and OpenAI/Anthropic APIs to frontend interfaces.
• Cost Optimization & Caching: Managing API costs, semantic caching, and fallback models (routing complex queries to GPT-4o, simple ones to smaller open models).
• Launching and Iterating: Packaging your utility, user feedback loops, and scaling your application.