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Generative AI Engineering 2026 : Foundational to Agentic AI
Rating: 4.2 out of 5(23 ratings)
1,073 students

Generative AI Engineering 2026 : Foundational to Agentic AI

Master GenAI, prompt engineering, transformers, RAG, diffusion models, LLMOps, and agentic AI with hands-on examples
Created by360DigiTMG Elearning
Last updated 12/2025
English

What you'll learn

  • The course provides the entire toolbox you will learn the course
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Apply your skills to real-life business cases

Course content

2 sections • 12 lectures • 1h 54m total length

  • Introduction to Generative AI and Prompt Engineering3:50

    This lecture introduces Generative AI and explains how it differs from traditional AI systems. It also covers the fundamentals of prompt engineering, why prompts matter, and how well-designed prompts influence accuracy, reasoning, and output quality of large language models.

  • Language Model Industry Overview13:46

    This lecture provides an overview of the language model industry, covering how language models evolved, key industry players, and how commercial and open-source models are shaping the AI ecosystem today.

  • Overview of Small Language Models (SLMs) and Tools7:05

    This lecture explains Small Language Models (SLMs), their architecture, benefits, limitations, and the tools commonly used to work with them in real-world applications.

  • Why Data and AI Readiness (DAR) Is Important3:47

    This lecture focuses on why data and AI readiness (DAR) are critical for successful AI systems. It explains data quality, availability, governance, and how poor data directly impacts AI and GenAI outcomes.

  • Retrieval-Augmented Generation (RAG) – Flowchart Explanation9:13

    This lecture explains Retrieval-Augmented Generation (RAG) using a clear flowchart. It shows how external knowledge is retrieved and combined with LLMs to produce more accurate, up-to-date, and reliable responses.

  • Fine-Tuning vs RAG: A Comparative Analysis6:44

    This lecture compares fine-tuning and RAG in detail, explaining when to use each approach based on cost, scalability, maintenance, and business requirements.

  • Generative AI: Quick Insights and Key Concepts8:57

    This lecture provides quick but essential insights into Generative AI, summarizing key concepts, terminologies, and ideas that help learners connect the dots across topics.

  • Autoencoders and Seq2Seq Models Explained9:35

    This lecture explains autoencoders and sequence-to-sequence models, focusing on how data is encoded, transformed, and decoded for learning representations.

  • Attention Mechanism: Concepts and Working12:10

    This lecture introduces the attention mechanism, explaining why it was needed, how it works conceptually, and how it improves context understanding in neural networks.

Requirements

  • No Programming experience Needed
  • No prior experience is required. We will start from the very basics
  • You need to download few softwares that is required to learn the course.

Description

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 GenAI

  • Build and fine-tune diffusion models using UNet

  • Work with foundation models and handle restricted enterprise data

  • Design, evaluate, and operate agentic AI systems

  • Compare popular LLM frameworks

  • Apply Ethical AI principles and understand LLM security threats

Prerequisites

This course is beginner-friendly, but the following is helpful:

  • Basic understanding of Python (recommended)

  • Familiarity with machine learning or NLP concepts (optional)

  • Curiosity to explore AI systems and real-world use cases

  • No prior GenAI or LLM experience required

Who this course is for:

  • Beginners for all age group people
  • Anyone who is curious to learn about Data Science & CRISP - ML(Q)
  • This course is for you if you want a great career

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