Generative AI & LLMs
Transformers, Prompt Engineering, RAG Pipelines & Multi-Modal Models
An elite 2–3 month immersion into cutting-edge Generative AI, foundational Large Language Models (LLMs), and agentic product engineering.
Dive deep into the Transformer architecture, multi-head self-attention mechanisms, and the mathematical mechanics driving modern LLMs.
Master production-grade prompt engineering, vector embeddings, semantic search, and state-of-the-art Retrieval-Augmented Generation (RAG) pipelines.
Fine-tune models with LoRA/QLoRA, integrate multi-modal diffusion engines (image/audio), and ship a deployable GenAI application.
Program Details
Program Roadmap
Foundations of Generative AI
Understand GenAI fundamentals, contrasting generative and discriminative paradigms and navigating the model landscape.
- ●Generative vs Discriminative AI: Probability Models and Sampling
- ●Evolution of NLP: From N-Grams and Recurrent Networks to LLMs
- ●The Open-Source and Proprietary Foundation Model Ecosystem
- ●Evaluating Generative Models: Benchmark Metrics and Human Feedback (RLHF)
- ●AI Safety, Guardrails, and Responsible Model Deployment Standards
Transformer Architecture
Understand how LLMs work internally by dissecting the attention mechanism and transformer mechanics.
- ●Intuition and Mathematics of Scaled Dot-Product Self-Attention
- ●Multi-Head Attention Mechanisms and Feed-Forward Neural Blocks
- ●Positional Encodings: Sinusoidal, Rotary Position Embeddings (RoPE), and Context Scaling
- ●Architectural Archetypes: Encoders, Decoders, and Encoder-Decoder Hybrids
- ●Subword Tokenization Algorithms and Vocabulary Management
Working with LLMs
Build LLM-powered applications using advanced prompting, API orchestration, and vector embeddings.
- ●Advanced Prompt Engineering: Few-Shot, Chain-of-Thought, and ReAct Patterns
- ●API Integration with Frontier Models like OpenAI, Anthropic, and Groq
- ●Function Calling, Structured JSON Outputs, and Tool Orchestration
- ●Vector Embeddings: High-Dimensional Geometry and Similarity Metrics
- ●Application Frameworks: LangChain and LlamaIndex for LLM Workflows
Fine-tuning & RAG
Customize LLMs for domain-specific tasks using Retrieval-Augmented Generation and parameter-efficient fine-tuning.
- ●Retrieval-Augmented Generation (RAG) Architecture and Vector Database Setup
- ●Document Chunking Strategies, Semantic Indexing, and Re-Ranking
- ●Decision Matrix: When to Prompt, When to RAG, and When to Fine-Tune
- ●Supervised Fine-Tuning and Domain Instruction Datasets
- ●Parameter-Efficient Fine-Tuning with LoRA and QLoRA on Modern GPUs
Image & Audio Generation
Explore multi-modal generative AI, including diffusion models, text-to-image, and audio synthesis.
- ●Multi-Modal AI Architecture: Vision-Language Models and Cross-Attention
- ●Diffusion Model Foundations: Forward Noise and Reverse Denoising Dynamics
- ●Latent Diffusion Architectures and Image Generation Workflows
- ●Audio Synthesis and Speech-to-Text Transcription with Whisper
- ●Multi-Modal Agent Pipelines Combining Vision, Audio, and Text
Capstone Project
Build, package, and deliver a deployable, production-ready GenAI product.
- ●End-to-End GenAI Product Scoping and Architectural Design
- ●Implementing an Enterprise RAG Engine or Domain Assistant
- ●Security Layers: Prompt Injection Defense and Response Guardrails
- ●FastAPI Backend and Modern Web Interface Deployment
- ●Full-Stack Deployment, Cloud Hosting, and Project Portfolio Showcase
From Learning to Production
What You'll Gain
Generative AI foundations
understand generative vs discriminative architectures and the evolving GenAI ecosystem
Deep Transformer mastery
decode multi-head self-attention, positional encoding, and transformer internals from scratch
LLM application engineering
master prompt design, function calling, tool use, and OpenAI/Anthropic SDKs
Production RAG architectures
vector databases (Pinecone, Chroma), chunking strategies, and hybrid semantic retrieval
Fine-tuning expertise
parameter-efficient fine-tuning (PEFT, LoRA, QLoRA) on custom domain datasets
Multi-modal generative AI
explore Stable Diffusion, latent diffusion models, and audio generation pipelines
Deployable capstone product
engineer and deploy a production-ready, full-stack Generative AI application