🏫 Programming & Software Development

Advanced Generative AI Agent & LLM Engineering

Mastering Transformer Architectures, RAG Systems, Fine-Tuning, and Multi-Agent Orchestration

Duration

12 Weeks

Weekly Hours

4 Hours

Course Incharge

Muzammil Bilwani

Advanced Generative AI Agent & LLM Engineering

📋 Prerequisites

✓ Advanced (Requires Python & API knowledge)

📖 Course Description

Deep dive into the technical core of LLMs and AI agents. Learn to build production-grade RAG pipelines, fine-tune models, and orchestrate complex multi-agent swarms using state-of-the-art frameworks.

What You Will Learn

Deeply understand Transformer architectures and attention mechanisms

Implement production-grade RAG and GraphRAG systems

Fine-tune LLMs using PEFT/LoRA and RLHF/DPO techniques

Orchestrate multi-agent swarms with LangGraph and CrewAI

Manage LLMOps, model deployment, and AI security

Build multimodal agents using vision and audio pipelines

Course Outline

The Transformer Deep-Dive

→Understanding "Attention is All You Need": Query, Key, and Value vectors
→Encoder-only vs. Decoder-only architectures (BERT vs. GPT)
→Positional Encoding and Multi-head Attention mechanisms
→Hands-on: Build a simplified Attention mechanism from scratch in Python

Tokenization & Embedding Spaces

→BPE (Byte Pair Encoding) and WordPiece tokenization strategies
→Visualizing High-Dimensional Vector Spaces and Cosine Similarity
→Static vs. Contextual Embeddings
→Hands-on: Build a semantic search engine using raw embeddings

The LLM Landscape & API Orchestration

→Benchmarking State-of-the-Art (SOTA) models: GPT-4o, Claude 3.5, and Llama 3.1
→Rate limits, Context Window management, and Token cost optimization
→Streaming responses and Asynchronous API calls
→Hands-on: Build a high-performance gateway to rotate between multiple LLM providers

Local Inference & Quantization

→Understanding FP16, INT8, and GGUF quantization formats
→Deploying local models using Ollama, vLLM, and LM Studio
→GPU Memory Management: VRAM requirements for 7B, 13B, and 70B models
→Hands-on: Set up a local private LLM server with an API endpoint

Masterclass in LangChain

→The Expression Language (LCEL): Building modular AI chains
→Document Loaders, Text Splitters, and Vector Stores
→Managing Conversation Memory: Windowed vs. Summary memory
→Hands-on: Build a "Memory-Enhanced" personal assistant that remembers user preferences

LlamaIndex for Data Intelligence

→Building Advanced Indexes: Tree, List, and Keyword tables
→Query Engines vs. Chat Engines
→Data Connectors (LlamaHub): Ingesting Notion, Slack, and Discord data
→Hands-on: Build a "Second Brain" app that queries your entire personal workspace

Logic Stepping & Reasoning (CoT)

→Implementing Chain-of-Thought (CoT) and Tree-of-Thought (ToT) prompting
→ReAct Logic: Synergizing Reasoning and Acting in LLMs
→Implementing Self-Correction loops in prompts
→Hands-on: Build a complex math and logic solver that "thinks aloud" before answering

Semantic Routing & Intent Classification

→Building routers to send queries to specialized small models
→Dynamic prompt selection based on user intent
→Balancing latency and accuracy with model routing
→Hands-on: Build an AI Customer Support Router that directs queries to Sales, Support, or Tech bots

Vector Database Engineering

→Production-grade Vector DBs: Pinecone, Milvus, and Weaviate
→Metadata Filtering and Hybrid Search (Keyword + Semantic)
→Indexing strategies for millions of vectors
→Hands-on: Build a scalable knowledge base for a legal or medical database

Chunking & Retrieval Optimization

→Semantic Chunking vs. Recursive Character splitting
→Parent Document Retrieval (PDR) and Contextual Compression
→Solving the "Lost in the Middle" problem in long context windows
→Hands-on: Optimize a RAG pipeline to increase retrieval accuracy by 40%

Re-ranking & Evaluation (RAGAS)

→Using Cross-Encoders for high-precision re-ranking
→Evaluating RAG with RAGAS (Faithfulness, Relevancy, Answer Correctness)
→Synthetic Test Data Generation for RAG evaluation
→Hands-on: Build a RAG testing suite to benchmark different retrieval strategies

GraphRAG & Knowledge Graphs

→Introduction to Neo4j and Knowledge Graph construction
→Combining Vector Search with Graph Traversal (GraphRAG)
→Extracting Entities and Relationships from unstructured text
→Hands-on: Build a Graph-based AI that maps connections between complex research papers

Dataset Engineering for Fine-Tuning

→Sourcing and Cleaning instruction datasets
→Using LLMs to generate high-quality synthetic training data
→Formatting data for JSONL (ChatML) and Alpaca formats
→Hands-on: Create a custom 5,000-row dataset for a niche industry bot

PEFT & LoRA (Parameter-Efficient Fine-Tuning)

→Why we don't full-fine-tune: Understanding LoRA and QLoRA
→Adapters and Merging: How to add skills to a model without overwriting it
→Training on Hugging Face using AutoTrain or Axolotl
→Hands-on: Fine-tune Llama-3 to speak in a specific brand voice or persona

RLHF & Preference Alignment

→Introduction to Reinforcement Learning from Human Feedback (RLHF)
→DPO (Direct Preference Optimization) vs. PPO
→Aligning models with safety and specific utility constraints
→Hands-on: Implement a DPO training loop to align a model with specific user preferences

Model Deployment & Serving (LLMOps)

→Deploying fine-tuned models with TGI (Text Generation Inference) or vLLM
→Model Versioning and A/B Testing in production
→Monitoring for "Model Drift" and "Hallucination Spikes"
→Hands-on: Deploy your fine-tuned model as a scalable Dockerized API

Agentic Reasonings & Function Calling

→Teaching AI to "Use Tools": Python Interpreter, Search, and SQL
→Pydantic Programs: Forcing LLMs to output strictly structured data
→Handling tool errors and retries autonomously
→Hands-on: Build an AI Agent that can query a SQL database and plot a chart in Python

Multi-Agent Frameworks (CrewAI)

→Designing Role-Based Agents: Manager, Researcher, and Writer
→Task Delegation and Inter-agent communication
→Hierarchical vs. Sequential agent workflows
→Hands-on: Build a "Content Agency Swarm" that researches, writes, and audits articles

Advanced Agents (AutoGen & LangGraph)

→Building stateful, cyclic agent graphs with LangGraph
→Collaborative problem solving with Microsoft AutoGen
→Human-in-the-loop: Implementing an "Approve/Reject" step for agents
→Hands-on: Build an Autonomous Software Engineer agent that writes, tests, and fixes code

Agent Memory & Planning (MemGPT)

→Implementing Virtual Context Management (OS-like memory)
→Long-term archival memory for agents
→Hierarchical Planning: Breaking 1-month goals into daily agent tasks
→Hands-on: Build a "Forever-Bot" that remembers your interactions over months of data

AI Security & Red Teaming

→Protecting against Prompt Injection and Data Exfiltration
→Implementing Guardrails (NeMo Guardrails, Llama Guard)
→Detecting PII (Personally Identifiable Information) in AI outputs
→Hands-on: Attempt to "jailbreak" your own agent and build a defense layer

Multimodal AI (Vision, Audio, Video)

→Integrating GPT-4V and Claude Vision into agent workflows
→Audio-to-Audio pipelines (Whisper + LLM + ElevenLabs)
→Video Analysis and Scene Understanding with AI
→Hands-on: Build a "Visual Security Agent" that describes video feeds in real-time

Capstone Phase 1: Architecture & Data

→Project Selection: Enterprise RAG, Multi-Agent Startup, or Specialized Fine-tune
→System Architecture Design and Tech Stack selection
→Data Ingestion and Pipeline setup

Capstone Phase 2: Deployment & Showcase

→Finalizing the Production-grade AI System
→Performance Tuning and Latency Optimization
→The Grand Demo: Presenting your Autonomous AI System to industry experts

📊 Grading Criteria

Component	Percentage
Quizzes	20%
Class Participation / Attendance	15%
Projects	25%
Final Projects	40%
Total	100%

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