Generative AI with LLMs, Prompting, Automation & Agents

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