This module covers the fundamentals of attention mechanisms, the evolution of transformers, and major LLMs like GPT, PaLM, and LLaMA. It includes instruction-tuned models, API integration, and real-world applications. You’ll also explore the open-source LLM ecosystem, model comparisons, Hugging Face, and key ethical considerations.

This module covers prompt engineering essentials, advanced prompting techniques like few-shot, zero-shot, and chain-of-thought, and strategies for optimizing generative AI outputs. You’ll learn how vector databases (ChromaDB, Pinecone, and Weaviate) enable semantic search and Retrieval-Augmented Generation (RAG). Hands-on work with LangChain shows how to build modular AI apps using prompt templates, tools, and agents for practical, state-of-the-art solutions.

This module covers fine-tuning and optimizing generative models, including basics like data augmentation and hyperparameter tuning, and advanced methods such as PEFT, LoRA, and QLoRA for efficient adaptation. You’ll learn how to evaluate models using metrics like BLEU and ROUGE, balancing quantitative and qualitative assessments. The course also introduces building and deploying AI solutions with LLMOps and industry best practices for real-world use.

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz, project, and labs.