Module 1: Foundations of Generative AI (4 Hours)

Focus: Mastering the ecosystem, prompt engineering, and the development environment.

(1.1) The Generative AI Ecosystem :

• LLM Architecture: Understanding Transformers, Tokens, and Context Windows.
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• Open vs. Closed Source: Navigating HuggingFace vs. OpenAI/Anthropic APIs.
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• Environment Setup: Python virtual environments, API Key management, and Jupyter bestpractices.

(1.2) Advanced Prompt Engineering :

Prompting Strategies: Zero-shot, Few-shot, and Chain-of-Thought (CoT).

• System Prompts: Defining robust personas and operational boundaries.

• Project: Building a ”Language Tutor” using advanced persona prompting.

Module 2: RAG Pipelines & Vector Databases (5 Hours)

Focus: Grounding AI in private data to eliminate hallucinations.

(2.1) Data Engineering for AI :

• Embeddings Explained: Transforming text into vector representations.

• Vector Stores: Implementation with ChromaDB and Pinecone.

• Chunking Strategies: Recursive Character Splitting vs. Semantic Splitting.
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(2.2) Retrieval Architectures :

• RAG Logic: The Retrieve-Augment-Generate workflow.

• Advanced Retrieval: Implementing Hybrid Search and Reranking.

• Project: Building a ”PDF Chatbot” capable of querying complex documents.

Module 3: Fine-Tuning & Specialized Models (5 Hours)

Focus: Customizing models for specific tasks and modalities.
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(3.1) Fine-Tuning LLMs :

• When to Fine-tune: Trade-offs between RAG and Fine-tuning.

• PEFT Techniques: Efficient training using LoRA and QLoRA.

• Dataset Preparation: Formatting JSONL data for training.

(3.2) Multimodal AI (Computer Vision) :

• Vision Models: Working with GPT-4o Vision and Open Source alternatives.

• Image Generation: Basics of Diffusion models.

• Project: Building a ”Visual Q&A System” that analyzes images.

Module 4: Autonomous Agents & LangGraph (6Hours)

Focus: Moving from linear Chains to cyclic, stateful Graphs.

(4.1) Tools & Function Calling :

• Function Calling API: Teaching LLMs to use calculators, search, and APIs.

• Custom Tools: Using decorators to wrap Python functions for agents.

• The ReAct Loop: Reason → Act → Observe architectures.

(4.2) LangGraph Architecture (NEW) :

• Chains vs. Graphs: Why production agents need loops, not just lines.

• State Management: Defining a global TypedDict state schema.

• Cyclic Flows: Implementing ”Self-Correction” loops (e.g., if code fails, try again).

• Persistence: Adding ”Memory” to agents using Database Checkpointers.‍‍

Module 5: Multi-Agent Orchestration (5 Hours)

Focus: Orchestrating teams of agents for complex enterprise tasks.

(5.1) Multi-Agent Patterns (NEW) :

‍• The Supervisor Pattern: Building a central ”Manager” agent that routes tasks to workers.

• Reliability Engineering: Using Pydantic for strict Structured Output (JSON).

• Handoffs: Techniques for passing state between specialized agents (e.g., Researcher → Writer).

(5.2) Capstone Project: Autonomous Competitor Analyst :

• Objective: Build a Supervisor-Worker system that autonomously researches a company andwrites a report.

Architecture:

• Supervisor: Orchestrates the workflow.

• Research Agent: Uses Tavily Search API to gather live data.

• Writer Agent: Compiles findings into a markdown report.

• Outcome: A fully functional, self-correcting multi-agent system.