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LLM Mastery: ChatGPT, Gemini, Claude, Llama, OpenAI & APIs

Name: LLM Mastery: ChatGPT, Gemini, Claude, Llama, OpenAI & APIs
Rating: 4.3 (1272 reviews)

Basics to AI-Agents: OpenAI API, Gemini API, Open-source LLMs, GPT-5, RAG, LangChain Apps, Colab and Prompt Engineering

Created byArnold Oberleiter

Last updated 11/2025

English

What you'll learn

Functionality of LLMs: Parameters, Weights, Inference, and Neural Networks
Understanding Neural Networks
Operation of Neural Networks with Tokens in LLMs
Transformer Architecture and Mixture of Experts
Fine-Tuning and the Creation of the Assistant Model
Reinforcement Learning (RLHF) in LLMs
LLM Scaling Laws: GPU & Data for Improvements
Capabilities and Future Developments of LLMs
Use of Tools by LLMs: Calculator, Python Libraries, and More
Multimodality and Visual Processing with LLMs
Multimodality in Language as in the Movie 'Her'
Systems Thinking and Future Prospects for LLMs
Self-Improvement after AlphaGo (Self-Improvement)
Improvement Possibilities: Prompts, RAG, and Customization
Prompt Engineering: Effective Use of LLMs with Chain of Thought and Tree of Thoughts Prompting & More
Adaptation of LLMs through System Prompts and Personalization with ChatGPT Memory
Long-Term Memory with RAG and GPTs
The GPT Store: Everything You Need to Know
Using GPTs for Data Analysis, PDFs, or Tetris Programming
Embeddings and Vector Databases for RAG
Integrating Zapier Actions in GPTs
Open-Source vs. Closed-Source LLMs
API Basics
Usage of the Google Gemini API and Claude API
Microsoft Copilot and Its Use in Microsoft 365
GitHub Copilot: The Solution for Programmers
The OpenAI API: Features, Pricing Models, and Everything You Need to Know About the OpenAI API Including App Creation
Introduction to Google Colab for API Calls to OpenAI
Creation of AI Apps and Chatbots with Langchain, Flowise, Vectorshift, LangGraph, CrewAI, Autogen, Langflow & more
Creation of AI Agents for Various Tasks like Social Media Contetn with Agency Swarm and Langchain Agents
Security in LLMs: Jailbreaks and Prompt Injections & more
Comparison of the Best LLMs
Google Gemini in Standard Interface and Google Labs with NotebookLM
Claude by Anthropic: Overview
Everything About Perplexity and POE
OpenAI Playground: Features, Billing Account & Temperature of LLMs
Google Gemini API: Video Analysis and More
Open-Source LLMs: Models and Use of Llama 3, Mixtral, Command R+, and Many More
HuggingChat: Interface for Open-Source LLMs
Running Local LLMs with Ollama and Building Local Rag Chatbots
Groq: Fastest Interface with LPU
Installation of LM Studio for Using Local Open-Source like Llama3 LLMs for Maximum Security
Using Open-Source Models in LM Studio and Censored vs. Uncensored LLMs
Fine-Tuning an Open-Source Model with Huggingface
Creating Your Own Apps via APIs in Google Colab with Dall-E, Whisper, GPT-4o, Vision, and More
Microsoft Autogen for AI Agents
CrewAI for AI Agents
Flowise with LangChain Function Calling
OpenAI Assistant API with function Calling for AI-Agents in different Frameworks
Flowise with Open-Source LLM as ChatBot
Security in LLMs and Methods to Hack LLMs
Future of LLMs as Operating Systems in Robots and PCs

Course content

15 sections • 161 lectures • 19h 50m total length

Welcome0:17
Course Overview10:07
My Goal and Some Tips3:10
Update on new Models1:04
Explanation of Links and Downloads3:09
Important Links1:20
Instructor Introduction: Arnold Oberleiter (Arnie)1:20

What This Section Is About?0:50
An LLM Consists of Only Two Files Parameter File and a Few Lines of Code7:21
An LM is basically two files: a 140GB parameter file and a 500-line run file, enabling local or cloud inference for open-source models like llama 2/3.
How Are the Parameters Created Pretraining (Initial Training of the LLM)7:21
What Is a Neural Network and how it works?8:47
Learn how neural nets use weights and neurons to transform inputs—like 28x28 pixel images or language tokens—into probability outputs via forward and back propagation, and how training adjusts weights.
How a Neural Network Works in an LLM with Tokens6:50
The Transformer Architecture Is Not Fully Understood (Yet?)8:24
Explore why transformer models hallucinate and how their outputs arise from probabilistic word predictions, despite limited understanding of the underlying architecture, and how pre-training and fine-tuning shape capabilities.
Other Possibilities of the Transformer Architecture: Mixture of Experts Explaied5:54
After Pretraining Comes Finetuning: The Assistant Model Is Created9:42
Transform pre-trained models into assistant models through fine-tuning after pre-training by training on high-quality, human-generated user and assistant examples.
The Final Step: Reinforcement Learning (RLHF)13:13
Learn how reinforcement learning works, including RL and reinforcement learning from human feedback, through simple reward-based examples and stories from ChatGPT, Hugging Face, and AlphaGo's milestones.
LLM Scaling Laws: To Improve LLM, We Only Need Two Things, GPU & Data9:01
Review: What Have You Learned So Far!3:55

What This Section Is About?0:55
LLMs Can Use Various Tools, Like Calculators, Python Libraries, etc.8:16
Multimodality, Visual Processing (Vision), and Image Recognition9:06
Multimodality with Language Like in the Movie "Her"5:09
What Could Happen in the Future? Systems Thinking! [Thinking Fast and Slow]5:02
Updates: ChatGPT Search, Canvas, & o1 (System thinking / Test-Time-Compute)13:37
Explore the latest updates in ChatGPT search, canvas, and o1 preview. Learn how system thinking and test-time compute enhance web search, coding, and editing.
Self-Improvement Inspired by AlphaGo2:00
Explore self-improvement inspired by AlphaGo Zero, which learns via self-play with no human data, and discuss future use of system two thinking and prompt engineering to advance LMS.
Further Ways to Improve LLMs: Prompts, RAG, Customization/System Prompts4:06
Improve llms with three simple methods: prompt engineering, retrieval augmented generation with vector databases and embeddings, and system prompts across models and interfaces.
LLMs as the New Operating System: What the Future Could Look Like24:15
Review: What Have You Learned in This Section.3:30
Llm mastery explains how models use tools like calculators, Python libraries, and internet to fetch data, chart results, and enable multimodal, system two thinking, and ai agents with rock technology.

What This Section Is About and the Interface of LLMs4:59
Master prompt engineering across every LM, learning best practices to guide AI understanding and interact with standard interfaces like ChatGPT, Hugging Chat, Gemini, and open-source models.
What is the Token Limit and why is it important6:13
Explore how tokens define input and output limits, how tokenization works, and why model token limits—from 4,000 to 128,000 tokens—shape chat memory and prompting strategies.
Why Is Prompt Engineering Important? An Example!3:14
Prompt Engineering Basics: Semantic Association4:03
Explore semantic association as the core of prompt engineering, showing how a single word activates many related concepts in ChatGPT and other LMs. Demonstrate how context guides searches and specificity.
Prompt Engineering for LLMs: The Simplest Strategies (Structured Prompts)5:38
3 Important "hacks" for Prompt Engineering and the Instruction Pormpting6:34
Master instruction prompting with three practical hacks: think step by step, take a deep breath, and use motivating prompts to boost prompt engineering and AI outputs.
Role Prompting in ChatGPT and other LLMs6:14
Shot Prompting: Zero-Shot, One-Shot und Few-Shot6:57
Explore zero-shot, one-shot, and few-shot prompting to optimize prompts for ChatGPT, including how to provide examples, structure descriptions, and leverage semantic associations.
Reverse Prompt Engineering and the "OK" Trick9:43
Master reverse prompt engineering and shot prompting, following a four-step formula that includes role setup, stepwise thinking, and token-saving strategies to extract prompts from text.
Chain of Thought Prompting: Step by Step to the Goal8:14
Tree of Thoughts (ToT) Prompting10:42
Master tree of thought prompting by generating solutions from math, emotional, and negotiation expert perspectives, selecting the most logical path and crafting a salary negotiation conversation.
The Combination of Prompting Concepts2:55
Real-World Use Cases for Large Language Models6:44
Claude Fable 5 and Sonnet 51:56
Review and a bit of Homework.1:38
Review the token limit and prompting techniques like semantic association and tree of thought. Complete the homework by applying one technique in your preferred LM and consider sharing the course.

What This Section Is About1:06
The Simplest Form of Personalization: ChatGPT Memory5:00
Customization Through System Prompts and Custom Instructions11:03
In-Context Learning: Short-Term Memory as Simple as Possible3:33
In-Context Learning: "The Short-Term Memory" but Efficient with SPR12:43
Harness sparse prime representation to compress large texts into the context window, enabling token-efficient in-context learning for LLMs like ChatGPT, with semantic associations and decompression.
Embeddings and Vector Databases for RAG: A Detailed Explanation4:14
Long-Term Memory with RAG: As Simple as Possible with GPTs & RAG8:04
The GPT Store: Everything You Need to Know & Testing of GPTs for Code, PDFs & YT13:29
Explore the GPT store to discover and test GPTs for code, PDFs, and YouTube, including features like weekly trending items, and tools such as a YouTube summarizer and code copilot.
Three ways to make Money with GPTs6:59
First: You need a Builder Profile to generate Leads from GPTs8:31
Create a GPT with Knowledge that can generate Leads and makes Upsells20:49
What is a API?3:59
Explore the concept of an api as a connection point that lets software components communicate through a contract of service between a client and a server. Discover how requests and responses, guided by documentation, enable app interactions and set up future integrations with ChatGPT and other apis.
Zapier Actions in GPTs: Automate Gmail, Google Docs, & more with the Zapir API17:33
Explore how sapir actions integrate with GPTs to automate tasks via Zapier, triggering Gmail, Google Docs, Drive, and more through webhooks and API imports.
How to Integrate Every API in your GPT8:14
Summary: What You Have Learned in This Section3:00
Explore LMS customization with memory and system prompts, in-context learning, and Ruk-based training; use embeddings and vector databases, then deploy GPTs via the GPT store, builder profile to generate leads.

Open-Source vs. Closed-Source LLMs4:27
What is the difference: Parameters, Architecture, Pretraining size & more14:28
Explore how model size, architecture, pre-training tokens, and context length shape language model performance, compare open-source options, and learn why fine tuning, safety, and multimodal capabilities matter.
Google Gemini in the Standard Interface: Everything you need to know15:38
Google Labs with NotebookLM: The Best Method to Learn Books5:08
Explore how Google Labs NotebookLM uses Gemini to upload books from Google Drive and generate fast, precise summaries by chatting with PDFs, text files, and other uploaded content.
Claude by Anthropic: An Overview4:43
The Leading Companies Are OpenAI, Google & Anthropic: Many Are Building on Them2:28
Perplexity: Advantages and Disadvantages, and Applications6:12
Poe, The Versatile All-in-One Platform9:02
What is the Microsoft Copilot: How it works and is my Data Save?10:49
Explore how Microsoft Copilot harnesses OpenAI models via API calls, integrates with Microsoft 365 apps, and safeguards data, while covering grounding, tokenization, and privacy guarantees.
Using Microsoft Copilot in the Web Interface5:37
Microsoft Copilot PCs3:55
Microsoft 365: Differences Between Free and Paid Subscription11:58
The Right Copilot Subscription and a Free Alternative.16:41
Copilot in Microsoft Word: Write Faster Than Ever16:15
Copilot in Microsoft PowerPoint: The Quick Presentation6:17
Copilot in Microsoft Outlook: Write and Reply to Your Emails Faster6:46
Copilot in Microsoft Excel: Big Possibilities but Still a Bit Early7:51
Microsoft Copilot GPT: Create your own personalized ChatBots10:19
GitHub Copilot: The AI Solution for Programmers5:25
Conclusion on Microsoft Copilot2:49
Review of the Closed-Source LLMs3:50

What Is This About? APIs of Closed-Source LLMs1:06
Overview of the OpenAI API6:01
Pricing Models of the OpenAI API8:33
Important: OpenAI Playground overview and Billing Account7:09
Get a tour of the OpenAI playground, learn to test models, and set up a billing account with credit card payment, rate limits, and email notification to protect your credits.
The OpenAI Playgroundin action10:13
The Google Gemini API: Video Analysis and Other Features8:03
Overview of the Google Gemini API, its Vertex AI tools, model options like Gemini 1.5 Pro and Gemini Flash, and video analysis capabilities with media uploads and transcripts.
The Anthropic API for the Claude Models2:58
Summary of the Closed-Source APIs3:31

What Are Open-Source LLMs and Which Ones Are Available4:36
Huggingface: An Introduction6:50
HuggingChat: An Interface for Using Open-Source LLMs with Function Calling14:33
Learn to use hugging chat to access open-source LLMs with function calling, web search, and tools for code, image generation, and privacy in a free cloud interface.
Groq: The Fastest Interface with an LPU Instead of a GPU4:37
Installation of LM Studio for Opensource LLMs: You need GPU, CPU, Cuda, Ram6:19
Using Open-Source Models in LM Studio: Llama3, Mistral; Phi-3 & more14:09
Censored vs. Uncensored LLMs (Llama3 Dolphin)10:59
discover how uncensored open source LLMs with dolphin fine-tuning address bias in pre-training data, compare with closed models, and learn safe, private, local deployment.
Setting Up Your Own Local Server with LM Studio4:07
Ornith3:01
Finetuning an Open-Source Model with Huggingface or Google Colab6:32
What You Should Remember4:00

What Is This Section About2:20
GitHub Overview: Why You Should Have an Account3:15
Join GitHub, a thriving developer community and code library, sign up with email or Google, and learn to explore, search, and manage issues and pull requests, including free Colab notebooks.
Introduction to Google Colab6:50
What You Will Create in Google Colab3:33
Our First API Call to OpenAI in Google Colab (Text Generation)17:08
Learn to call the OpenAI API from Google Colab to generate text, install the library, configure an API key, and run model completions.
DALL-E via the OpenAI API in Google Colab (Image Generation)8:46
Text-to-Speech (TTS) with the OpenAI API in Google Colab9:23
Transcribing with Whisper via the OpenAI API in Google Colab5:17
Image Recognition with Vision via the OpenAI API in Google Colab5:15
Learn to use vision via the OpenAI API in Google Colab to describe images. Copy a few lines of code, set the image URL, and prompt what's in this image.
Overview of Your Entire Notebook and a few tricks6:35
Overview of the Google Colab notebook for OpenAI tools, setting up the client and API keys, and using GPT-4 Omni for text generation, DALL-E for images, and Whisper for speech-to-text.
What You Have Learned Here3:02

AI Agents: Definition and Available Tools for Creating Apps and Helpers14:10
The Vectorshift Interface for AI Agents and AI Apps10:18
The easy way to make a ChatBot in Vectorshift7:46
Knowledge Through RAG: Training the AI Agent on Data with Automatic Updates18:11
Learn knowledge through rag: train a chatbot on your data with automatic updates. Create a no-code pipeline using vector databases, embeddings, and knowledge bases to deploy a chatbot.
Bot Deployment: As a Standalone App, in WhatsApp, Slack, or on Websites18:07
Deploy your chatbot as a standalone app, a web page search bar, or a chat bubble, then configure, export, and embed it across websites and pages.
Overview of a AI Agent with Multiple Experts in Vectorshift14:38
Langchain, Langflow, CrewAI, Autogen.... What Do I Need and is Python important?3:07
Three Ways to Run Flowise: Locally with Node.js or Externally in the Claude5:42
Explore three ways to run flowise: locally with node, locally with an advanced folder install, or deploying to the cloud, with Node.js setup and cloud options.
Installing Flowise with Node.js (JavaScript Runtime Environment)7:07
The Flowise Interface: Simpler Than langflow, builds on Langchain & LangGraph5:49
Discover the flow wise interface, set dark mode, create and save jet flows, and explore agent flows and the marketplace, learn to implement local Q&A with vector stores and templates.
A Example for a Chatbot: Q&A Chain, Memory & RAG with Vectordatabase4:12
Function Calling, Memory & RAG: Simplified with the OpenAI Assistant API19:25
Installation of Ollama, downloading Llama3, and hosting it on a local server8:16
Local RAG Chatbot with Llama3 & Ollama: A Local Langchain App16:28
Build a local rec chatbot with llama3 on Ollama, using LangChain, a conversational retrieval Q&A chain, an in-memory vector store, embeddings, Cheerio web scraper, and a text splitter.
AI Agents Like with Langchain + LangGraph or Autogen & CrewAI (with Flowise)11:33
Langchain-Style AI Agent with different Experts for Social Media, Math and Code15:11
Use your Agent as a standalone Application3:39
Build a standalone local server app using the agent framework, with embed options for html and react interfaces. Prepare to host in the cloud, share your chatbot, and customize avatar.
Hosting Chatbots for Customers Externally on Render (Cloud Hosting)13:42
Embed a Chatbot into Websites: HTML, WordPress, Shopify Page & More10:03
Flowise Tips: Getting Leads, API Endpoints, Speech Recognition & More12:20
Creating a Free Chatbot with Open-Source Models and Tools: Mistral Mixture5:50
Build a chatbot using open-source models with hugging face inference, wiring an lm chain, prompts, and credentials, testing with mistral and jokes, while noting OpenAI is preferred for client projects.
Insanely fast inference with the Groq API8:27
Check Out the Marketplace: If Else Chain and More2:52
Overview of Microsoft Autogen, CrewAI and Agency Swarm on Github4:03
Review and What You Should Do4:02

Requirements

No prior knowledge required, everything will be shown step by step.

Description

Have you ever thought about how Large Language Models are transforming the world and creating unprecedented opportunities?

"AI won't take your job, but someone who knows how to use AI might," says Richard Baldwin.

Are you ready to master the intricacies of LLMs and leverage their full potential for various applications, from data analysis to the creation of chatbots and AI agents?

Then this course is for you!

Dive into 'LLM Mastery: ChatGPT, Gemini, Claude, Llama, OpenAI & APIs'—where you will explore the fundamental and advanced concepts of LLMs, their architectures, and practical applications. Transform your understanding and skills to lead in the AI revolution.

This course is perfect for developers, data scientists, AI enthusiasts, and anyone who wants to be at the forefront of LLM technology. Whether you want to understand neural networks, fine-tune AI models, or develop AI-driven applications, this course offers everything you need.

What to expect in this course:

Comprehensive Knowledge of LLMs:

Understanding LLMs: Learn about parameters, weights, inference, and neural networks.
Neural Networks: Understand how neural networks function with tokens in LLMs.
Transformer Architecture: Explore the Transformer architecture and Mixture of Experts.
Fine-Tuning: Understand the fine-tuning process and the development of the Assistant model.
Reinforcement Learning (RLHF): Dive into reinforcement learning with human feedback.

Advanced Techniques and Future Trends:

Scaling Laws: Learn about the scaling laws of LLMs, including GPU and data improvements.
Future of LLMs: Discover the capabilities and future developments in LLM technology.
Multimodal Processing: Understand multimodality and visual processing with LLMs, inspired by movies like "Her."

Practical Skills and Applications:

Tool Utilization: Use tools with LLMs like calculators and Python libraries.
Systems Thinking: Dive into systems thinking and future perspectives for LLMs.
Self-Improvement: Learn self-improvement methods inspired by AlphaGo.
Optimization Techniques: Enhance LLM performance with prompts, RAG, function calling, and customization.

Prompt Engineering:

Advanced Prompts: Master techniques like Chain of Thought and Tree of Thoughts prompting.
Customization: Customize LLMs with system prompts and personalize with ChatGPT memory.
Long-Term Memory: Implement RAG and GPTs for long-term memory capabilities.

API and Integration Skills:

API Basics: Understand the basics of API usage, including OpenAI API, Google Gemini, and Claude APIs.
Microsoft and GitHub Copilot: Utilize Microsoft Copilot in 365 and GitHub Copilot for programming.
OpenAI API Mastery: Explore functionalities, pricing models, and app creation with the OpenAI API.

AI App Development:

Google Colab: Learn API calls to OpenAI with Google Colab.
AI Agents: Create AI agents for various tasks in LangChain frameworks like Langgraph, Langflow, Vectorshift, Autogen, CrewAI, Flowise, and more.
Security: Ensure security with methods to prevent jailbreaks and prompt injections.

Comparative Insights:

Comparing Top LLMs: Compare the best LLMs, including Google Gemini, Claude, and more.
Open-Source Models: Explore and utilize open-source models like Llama 3, Mixtral, and Command R+ with the possibility of running everything locally on your PC for maximum security.

Practical Applications:

Embedding and Vector Databases: Implement embeddings for RAG.
Zapier Integration: Integrate Zapier actions into GPTs.
Open-Source LLMs: Install and use LM Studio for local open-source LLMs for maximum security.
Model Fine-Tuning: Fine-tune open-source models with Huggingface.
API-Based App Development: Create apps with DALL-E, Whisper, GPT-4o, Vision, and more in Google Colab.

Innovative Tools and Agents:

Microsoft Autogen: Use Microsoft Autogen for developing AI agents.
CrewAI: Develop AI agents with CrewAI.
LangChain: Understand the framework with divisions like LangGraph, LangFlow, and more.
Flowise: Implement Flowise with function calls and open-source LLM as a chatbot.

Ethical and Security Considerations:

LLM Security: Understand and apply security measures to prevent hacking.
Future of LLMs: Explore the potential of LLMs as operating systems in robots and PCs.

This course is ideal for anyone looking to delve deeper into the world of LLMs—from developers and creatives to entrepreneurs and AI enthusiasts.

Harness the transformative power of LLM technology to develop innovative solutions and expand your understanding of their diverse applications.

By the end of 'LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs' you will have a comprehensive understanding of LLMs, their applications, and the skills to harness their power for various purposes. If you are ready to embark on a transformative journey into AI and position yourself at the forefront of this technological revolution, this course is for you.

Enroll today and start your journey to becoming an expert in the world of Large Language Models!

Who this course is for:

To everyone who wants to learn something new and gain deep insights into LLMs
To entrepreneurs who want to become more efficient and save money
To individuals who are interested in AI and want to build their own models

LLM Mastery: ChatGPT, Gemini, Claude, Llama, OpenAI & APIs

What you'll learn

Explore related topics

Course content

Introduction and Overview7 lectures • 20min

How LLMs Work: Parameters, Weights, Inference, Neural Networks & More11 lectures • 1hr 21min

Additional Capabilities of LLMs & Future Developments10 lectures • 1hr 16min

Prompt Engineering: Effective Use of LLMs in the Standard Interface15 lectures • 1hr 26min

LLM Customization: System Prompts, Memory, RAG & Creating Expert Models or GPTs15 lectures • 2hr 8min

Closed-Source LLMs: An Overview of Available Models and how to use them21 lectures • 2hr 51min

APIs of Closed-Source LLMs8 lectures • 48min

Open-Source LLMs: Available Models and Their Use in the Claude & Locally11 lectures • 1hr 20min

First Steps to Creating Your Own Apps via APIs in Google Colab11 lectures • 1hr 11min

Apps, Chatbots, and AI Agents: Build cool Stuff for Automation and FUN25 lectures • 4hr 5min

Requirements

Description

Who this course is for: