COURSE OUTLINE

Day 1: Advanced Architectural Patterns & Optimization

Focus: Moving from basic prompts to efficient, high-performance systems.

• Evolution of Transformers: Deep dive into modern architectures (MoE - Mixture of Experts) and inference acceleration. 
• Reasoning Models (o-series): When and how to use models with internal "Chain of Thought" for complex logic.
• Optimization Strategies: Implementing Prompt Caching and Flash Attention to minimize token consumption.
• Structured Output Engineering: Forcing 100% schema compliance for downstream system integration.
• Hands-on: Benchmarking GPT-5.5 vs. Gemini 2.0 for reasoning-heavy engineering tasks.

Day 2: RAG 2.0 & Vector Infrastructure

Focus: Designing the "Long-Term Memory" of enterprise AI.

• Semantic Infrastructure: Deploying Vertex AI Vector Search at scale with HNSW indexing.
• Advanced Retrieval Strategies: Implementing Hybrid Search (Keyword + Semantic) and Cross-Encoders for re-ranking.
• Dynamic Grounding: Connecting models to real-world data via Google Search and BigQuery. 
• Evaluation: Using RAGAS and Ariadne to measure faithfulness and relevancy.
• Hands-on Lab: Building a RAG pipeline that handles 100,000+ technical documents with sub-second retrieval.

Day 3: Multi-Agent Orchestration & Systems Design

Focus: Building "Digital Teams" that solve multi-step problems.

• Agentic Design Patterns: ReAct (Reason+Act), Plan-and-Execute, and Tool-use protocols.
• Orchestration Frameworks: Deep dive into LangGraph (Stateful agents) and AutoGPT-style autonomy.
• Multi-Agent Collaboration: Managing communication, conflict resolution, and memory sharing between specialized agents.
• Hands-on Lab: Architecting a "Self-Healing DevOps Agent" that monitors logs, identifies bugs, and submits PRs autonomously.

Day 4: Domain-Specific Models & Model Distillation

Focus: Customizing intelligence for specialized industries. 

• Industry-Specific Models: Case studies on Med-PaLM (Healthcare), SecLM (Security), and FinGPT (Finance). 
• Parameter-Efficient Fine-Tuning (PEFT): Mastering LoRA and QLoRA for low-cost model adaptation. 
• Model Distillation: Teaching a smaller model (Gemma 2) to mimic the performance of a massive model (Gemini 1.5 Pro).
• Hands-on Lab: Distilling a specialized legal reasoning model into a smaller, deployable edge-device model.

Day 5: LLMOps, Security, and Production Scaling

Focus: Monitoring, securing, and shipping AI at scale.

• The LLMOps Flywheel: Model versioning, prompt management, and automated A/B testing.
• Security & Red Teaming: Defending against Indirect Prompt Injection and data exfiltration in agentic systems.
• Monitoring & Debugging: Tracking "Semantic Drift" and setting up real-time toxicity filters.
• Capstone Project: Final deployment of an autonomous, multi-agent enterprise solution.
• Technical Defense: Presenting the system's architecture, cost-analysis, and safety framework.