Generative AI Engineering 2026 : Foundational to Agentic AI

About this course

This course provides a comprehensive and end-to-end understanding of Generative AI (GenAI), Large Language Models (LLMs), and modern AI systems used in real-world applications. You will start with the foundations of AI and GenAI, exploring their origins, evolution, and the history of language models.

The course dives deep into prompt engineering, data importance, simple language models, and the tools that support them. You will gain a clear understanding of Retrieval-Augmented Generation (RAG), including why it is used, how it works, and how it compares with fine-tuning.

You will learn core deep learning architectures such as autoencoders, seq2seq models, and transformers, with step-by-step explanations of encoders, decoders, attention mechanisms, and real-world NLP examples. The course also covers LLMs, sentiment analysis, summarization, image-text similarity using CLIP, and modern diffusion models, including UNet implementation, training, inference, and fine-tuning.

Advanced topics include foundation models (OpenAI, Gemini), handling restricted data with GPT, GenAIOps, LLMOps, RAGOps, and building scalable AI solutions. You will also explore agentic AI, its lifecycle, evaluation, architecture design, and frameworks like LangGraph, CrewAI, Semantic Kernel, and Autogen.

The course concludes with Ethical AI development, LLM security practices, and real-world attack scenarios—making you industry-ready for modern AI roles.

Learning Objectives

By the end of this course, you will be able to:

• Understand the evolution of AI, GenAI, and language models
• Design effective prompts and understand prompt engineering workflows
• Explain RAG vs fine-tuning and choose the right approach

Understand transformer architecture in detail (encoder, decoder, attention)Apply NLP tasks such as sentiment analysis and summarization using GenAIBuild and fine-tune diffusion models using UNet<

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