Cutting-Edge LLMs and Multimodal AI

Explore the Frontiers of Intelligence—Master LLMs and Multimodal AI

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

Cutting-Edge LLMs and Multimodal AI is an advanced-level program crafted for AI professionals, researchers, and developers who want to stay ahead in the rapidly evolving landscape of generative and multimodal intelligence. The course dives into the architecture, capabilities, and real-world applications of the latest LLMs (like GPT-4, Claude, Gemini, and LLaMA) and their integration with vision, audio, and sensor modalities to build powerful, human-like systems.

Aim:

To provide in-depth knowledge and hands-on experience in advanced Large Language Models (LLMs) and multimodal AI systems that integrate text, image, speech, and video inputs for next-generation applications.

Program Objectives:

To advance learners’ understanding of modern LLM and multimodal architectures
To equip them with hands-on skills for building and deploying real-world AI systems
To explore use-cases across healthcare, law, media, and accessibility
To cultivate ethical, responsible practices in frontier AI development

What you will learn?

Week 1: Next-Gen LLMs – Capabilities, Architecture, and Trends

Module 1: Deep Dive into Modern LLMs

Chapter 1.1: Evolution from GPT-3 to GPT-4, Claude, Gemini, and beyond
Chapter 1.2: Transformer Enhancements (Mixture of Experts, Long-Context, LoRA)
Chapter 1.3: Performance Benchmarks and Trade-offs
Chapter 1.4: Open vs. Closed Models (Open-source innovations: LLaMA, Mistral, Mixtral)

Module 2: Advanced Prompting and Fine-Tuning

Chapter 2.1: Structured Prompting Techniques (Zero/Few-shot, CoT, Tool-Use)
Chapter 2.2: Retrieval-Augmented Generation (RAG) Overview
Chapter 2.3: Fine-Tuning vs. Instruction Tuning vs. RLHF
Chapter 2.4: Evaluation and Safety Alignment Metrics

Week 2: Foundations of Multimodal AI Systems

Module 3: Language + Vision Models

Chapter 3.1: Multimodal Transformers (BLIP-2, Flamingo, GPT-4V, Gemini)
Chapter 3.2: Vision Encoding and Alignment with Text Embeddings
Chapter 3.3: Image Captioning, Visual Q&A, Scene Understanding
Chapter 3.4: Visual Prompting, Layout Understanding, Image-to-Text Inference

Module 4: Language + Other Modalities

Chapter 4.1: Audio-Language Systems (Whisper, AudioCraft, VALL-E)
Chapter 4.2: Video-Language Interaction (Sora, Pika Labs, RunwayML)
Chapter 4.3: Code + Text and Structural Models (Code LLMs, ReAct)
Chapter 4.4: Multimodal Embeddings and Cross-Modal Retrieval

Week 3: Applications, Ethics, and Future Outlook

Module 5: Industrial Applications and Innovation

Chapter 5.1: Multimodal AI in Search, Design, Robotics, and Healthcare
Chapter 5.2: Tool-Use and API-Augmented Agents (Auto-GPT, OpenAgents, ReAct)
Chapter 5.3: Agent Simulations, Planning, and Toolchains
Chapter 5.4: Case Studies: Enterprise LLM Use and Multimodal Integrations

Module 6: Ethics, Policy, and the Frontier of AI

Chapter 6.1: AI Hallucinations, Safety, and Guardrails
Chapter 6.2: AI Copyright, Content Authenticity, and Watermarking
Chapter 6.3: Regulation Trends and Global AI Policies
Chapter 6.4: What’s Next: Multimodal General Intelligence and Open Challenges

Intended For :

AI/ML practitioners, data scientists, software engineers, researchers
Prior knowledge of Python, LLMs, and basic neural network concepts
Ideal for professionals building AI tools and cross-modal applications