Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Generative AI with LLMs, Prompting, Automation & Agents
Rating: 2.7 out of 5(4 ratings)
29 students

Generative AI with LLMs, Prompting, Automation & Agents

Learn how Generative AI models are revolutionizing content, code, innovation using prompt engineering & real-world tech.
Created byUplatz Training
Last updated 10/2025
English

What you'll learn

  • Understand what Generative AI is and how it works
  • Learn different types of generative models (GANs, VAEs, Transformers, etc.)
  • Explore how GenAI can generate text, images, audio, and video
  • Build and train basic generative models from scratch
  • Use pre-trained models like GPT and DALL·E for real-world tasks
  • Practice prompt engineering to get better results from AI models
  • Learn how to fine-tune models for specific use cases
  • Build GenAI applications like chatbots, content creators, and art generators
  • Understand the risks, ethics, and responsible use of GenAI

Course content

7 sections • 140 lectures • 95h 39m total length

  • Part 1 - Large Language Models (LLMs)45:22
  • Part 2 - Large Language Models (LLMs)36:22
  • Part 3 - Large Language Models (LLMs)31:53
  • Part 4 - Large Language Models (LLMs)26:40

    Explore how attention mechanisms power large language models by building self-attention from a simple variant to multi-attention, computing context vectors and training weights for GPT-like decoder architectures.

  • Part 5 - Large Language Models (LLMs)36:59

    Explore how large language models power generative ai, detailing prompting strategies, automation workflows, and agent-based applications.

  • Part 6 - Large Language Models (LLMs)19:03

    Explore extending single-ed attention to multi-ed attention in large language models, stacking causal attention modules or using a single integrated multi-ed class to produce parallel context vectors across edges.

  • Part 7 - Large Language Models (LLMs)25:31

    Learn to code an emblem-based, hdpt-like large language model with transformer blocks, layer normalization, and gelu activations to generate human-like text and assess parameter counts.

  • Part 8 - Large Language Models (LLMs)20:33

    Examine large language model building blocks by implementing gelu and comparing it with relu and swigul, then discuss transformer blocks, shortcut connections, and gpt architecture.

  • Part 9 - Large Language Models (LLMs)23:24

    Build and analyze a GPT-2 style large language model from token and position embeddings through transformer blocks to the final linear output layer, and examine memory footprint and decoding.

  • Part 10 - Large Language Models (LLMs)39:34

    Explore large language models (llms) and their place in the generative ai toolkit. Discover core concepts around llms and their application to prompting, automation, and agents.

  • Part 11 - Large Language Models (LLMs)30:15
  • Part 12 - Large Language Models (LLMs)18:45
  • Part 13 - Large Language Models (LLMs)23:36

    Explore fine-tuning approaches for pre-trained large language models, including classification and instruction fine-tuning, and prepare data to classify spam messages.

  • Part 14 - Large Language Models (LLMs)30:42
  • Part 15 - Large Language Models (LLMs)30:49
  • Part 16 - Large Language Models (LLMs)46:27
  • Part 17 - Large Language Models (LLMs)38:29

    Explore the architecture and applications of large language models (LLMs), and learn how prompting, automation, and agents unlock advanced capabilities in generative AI.

  • Part 18 - Large Language Models (LLMs)39:35

    Explore large language models (LLMs) and their role in generative AI, prompting, automation, and agent-based workflows.

  • Part 19 - Large Language Models (LLMs)23:33
  • Part 20 - Large Language Models (LLMs)24:13

    Explore large language models from scratch, covering tokenization, data loading, transformer configurations, training and evaluation losses, sampling strategies, fine-tuning with alpaca data and LoRa, and performance considerations.

  • Part 21 - Large Language Models (LLMs)32:14

Requirements

  • Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Generative AI with LLMs, Prompting, Automation & Agents course by Uplatz.


Generative AI (Generative Artificial Intelligence) refers to a type of artificial intelligence that is capable of creating new content—such as text, images, audio, code, and more—rather than simply analyzing existing data. It mimics human creativity by learning from large datasets and generating outputs that resemble original, human-made content.


What It Does

Traditional AI systems are good at recognizing patterns or making predictions based on existing data. Generative AI goes a step further by actually producing new data that didn't exist before. For example:

  • Writing articles or stories

  • Creating images or artwork

  • Composing music

  • Writing code

  • Designing products or layouts


How It Works

Generative AI typically relies on advanced machine learning techniques, especially deep learning models such as:

  • Transformers – used in models like GPT (text) or T5

  • Diffusion models – used in image generation (like DALL·E or Stable Diffusion)

  • GANs (Generative Adversarial Networks) – used for creating realistic media


A simplified breakdown of the process:


  1. Training

    • The model is trained on massive datasets (e.g., books, websites, images, code).

    • It learns statistical patterns, styles, and relationships in the data.

  2. Learning Probabilities

    • Instead of memorizing, the model learns the probability of what should come next in a sequence (next word, next pixel, etc.).

  3. Generation (Inference)

    • When you give it a prompt, it generates new content based on what it has learned.

    • For instance, if you type a sentence, a text model will complete it or write a full article.

    • If you input a concept, an image model can generate an image matching that description.

  4. Fine-Tuning

    • The base model can be refined using reinforcement learning or task-specific data to make it more accurate, aligned, or safer.


Generative AI with LLMs, Prompting, Automation & Agents - Course Curriculum


  1. Large Language Models (LLMs) - part 1 to 21

  2. Coding of Transformers and LLMs - part 1 to 41

  3. Prompt Engineering for Generative AI - part 1 to 19

  4. AI Workflow Automation - part 1 to 24

  5. AI Agents with Python - part 1 to 15

  6. RAG for Generative AI - part 1 to 5


Common Applications of Generative AI

1. Text Generation

  • Writing articles, blogs, and essays

  • Drafting emails and messages

  • Summarizing long documents

  • Translating languages

  • Answering questions or tutoring

2. Image Generation

  • Creating digital art and illustrations

  • Generating product mockups and logos

  • Designing ads, posters, and visual content

  • Style transfer and photo editing

3. Code Generation

  • Auto-completing code

  • Generating boilerplate scripts

  • Fixing bugs and refactoring code

  • Explaining code snippets

4. Audio and Music

  • Composing original music

  • Generating voiceovers or speech

  • Producing sound effects

  • Voice cloning and enhancement

Video Generation

  • Creating short films and animations

  • Generating explainer videos

  • Video summarization

  • Scene-to-video synthesis

5. 3D Modeling and Design

  • Generating 3D objects and environments

  • Designing virtual products or architecture

  • Game asset creation

6. Gaming

  • Procedural content and level generation

  • NPC (non-player character) behavior scripting

  • Dialogue generation

7. Fashion and Product Design

  • Designing apparel and accessories

  • Creating virtual try-ons

  • Generating custom product variants

8. Education

  • Personalized tutoring and explanations

  • Quiz and flashcard generation

  • Adaptive learning content

9. Marketing and Advertising

  • Writing ad copy and taglines

  • Creating personalized campaigns

  • Designing social media posts

10. Legal and Compliance

  • Drafting legal documents

  • Reviewing and summarizing policies

  • Identifying contract risks

11. Healthcare and Biotech

  • Generating radiology and diagnostic reports

  • Simulating molecular structures

  • Summarizing patient records

12. Customer Support

  • Chatbots for FAQs and ticket handling

  • Email and chat summarization

  • Response recommendation

13. Finance

  • Automating financial reports

  • Analyzing and summarizing earnings calls

  • Detecting unusual financial patterns


Benefits

  • Rapid content generation

  • Personalized or on-demand outputs

  • Automation of creative and technical tasks

  • Support for brainstorming and ideation

  • Time and cost efficiency for businesses


Challenges and Risks

  • May generate incorrect or misleading content

  • Can reflect biases from the training data

  • Risk of misuse for fake content or misinformation

  • Computational and environmental costs

  • Requires careful monitoring and human validation

Who this course is for:

  • AI/ML Engineers – looking to build or fine-tune generative models
  • Software Developers – wanting to integrate GenAI into applications
  • Data Scientists – interested in advanced modeling and content generation
  • Tech Enthusiasts & Hobbyists – curious about AI tools like ChatGPT, Midjourney, DALL·E
  • Product Managers – aiming to understand capabilities and limitations of GenAI
  • Startup Founders & Innovators – exploring GenAI use cases and MVP development
  • Content Creators & Designers – who want to leverage AI for creative work
  • Researchers & Academics – studying generative models and their applications
  • Business Professionals – interested in using AI to improve workflows and automation
  • Students & Career Changers – who want to enter the AI field with hands-on GenAI skills

Report abuse