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

Name: LLM Mastery: ChatGPT, Gemini, Claude, Llama, OpenAI & APIs
Rating: 4.3 (1301 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 7/2026

English

German [Auto],English [Auto],

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
Master the lifecycle of large language models from pre-training and fine-tuning to reinforcement learning and scaling laws, then explore multimodal capabilities, tool use, and prompt engineering for custom ai agents.
My Goal and Some Tips3:10
Increase playback speed to two or three times to boost focus. Spread AI knowledge and cover LMS from basics to advanced.
Update on new Models1:04
Explore new models such as cloud Sonnet 4.5 and GPT five grok on leaderboards, with open source models like minimax, GPT OSS, and Cwen models.
Explanation of Links and Downloads3:09
Explore all course resources in one place by accessing essential links and downloadable materials, including Hugging Face, GitHub, Google Colab, LM Studio, OpenAI Playground, and model tuning guides.
Important Links1:20
Instructor Introduction: Arnold Oberleiter (Arnie)1:20
Arnold Oberleitner introduces his background in ai, transformers, diffusion, and building chatbots and ai automations for companies and small businesses, alongside his German and English YouTube channels.

What This Section Is About?0:50
Explore the basics of language models, including two-file structure, pre-training, fine-tuning, neural nets, transformer architecture, reinforcement learning, and scaling laws, with local or cloud computing.
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
Learn how large language model parameters form in pre-training, by compressing ten terabytes of text into a 140 GB parameter file using 6000 GPU clusters and weeks of compute.
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
Discover how a neural network within an llm processes word tokens, uses weights to predict the next word by probability, and iterates tokens through the transformer.
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
Explore how the transformer architecture uses a router to route inputs to eight 7 billion parameter experts for code, writing, or math, boosting efficiency over a single giant model.
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
The lecture argues that scaling lms relies on more compute and data, and with ongoing algorithm improvements and reinforcement learning from machines, models get better while training costs fall.
Review: What Have You Learned So Far!3:55
Review the basics of large language models: run files, parameter files, and pre-training on vast text with transformer architecture. Explore fine-tuning, reinforcement learning, and reducing model hallucinations.

What This Section Is About?0:55
Explore how LMS platforms go beyond text to multimodal capabilities, acting as an operating system that can run programs, access Python libraries, generate images, videos, and music, and support self-improvement.
LLMs Can Use Various Tools, Like Calculators, Python Libraries, etc.8:16
Explore how LLMs use tools via function calling, calculators, Python libraries, code interpreter, and web searches to build charts, tables, and images.
Multimodality, Visual Processing (Vision), and Image Recognition9:06
Discover how vision lets ChatGPT understand pictures, upload images, and describe them clearly. The lecture demonstrates generating code and html from visuals using diffusion models and Grok vision, showcasing multimodality.
Multimodality with Language Like in the Movie "Her"5:09
Explore multimodality in language models, showcasing how chat prompts, voice output, and visual input enable talking, listening, and seeing like in the movie Her; plus building apps via APIs.
What Could Happen in the Future? Systems Thinking! [Thinking Fast and Slow]5:02
Explains the difference between system one thinking and system two thinking, using examples like 2+2 and 64×32, and shows how LMS could adopt step-by-step, chain-of-thought prompts for more thoughtful outputs.
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
Llms are the new operating system, reading and generating text, browsing the web, using tools and libraries, and creating images, videos, and music, while evolving into advanced, multimodal agents.
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 is the key to right answers, shown through a water-jug problem and token-based versus human reasoning. Use simple, step-by-step prompts and roles across LLMs.
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
Learn how structured prompts fuse a modifier, topic, and audience-focused modifiers to produce tailored, SEO-friendly outputs like blog posts, with clear examples.
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
Role prompting improves outputs across ChatGPT and other LLMs by assigning clear roles and context, leveraging semantic association for clearer, SEO-friendly results.
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
Learn how chain of thought prompting improves model reasoning by showing step-by-step calculations, with zero-shot and few-shot prompts and the 'let's think step by step' approach.
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
Explore how prompting concepts enable llm mastery across tasks like text creation, editing, translation, coding support, and data analysis, with practical lms applications and api integration.
Real-World Use Cases for Large Language Models6:44
Explore real-world applications of large language models by combining prompting concepts, leveraging semantic association, role prompting, structured prompts, and examples to generate high-quality outputs.
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
Personalize chatbots with ChatGPT in the standard interface, starting with memory customization and system prompts, then use vector embeddings, GPT stores, external APIs, and Zapier integration to send emails.
The Simplest Form of Personalization: ChatGPT Memory5:00
Activate memory to personalize ChatGPT and let it remember your preferences, like steak medium, across chats; manage, delete, or clear memory in settings personalization to customize interactions.
Customization Through System Prompts and Custom Instructions11:03
Tailor ChatGPT with system prompts and custom instructions to improve responses. Learn to enable and customize these prompts, choose plugins like browsing and Dall-E, and use shortcuts and tables.
In-Context Learning: Short-Term Memory as Simple as Possible3:33
Explore in-context learning and short-term memory in language models using system prompts and the context window to guide ChatGPT with LMS content, highlighting upsides and downsides.
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
Explore how embeddings turn text into vector representations stored in a vector database, enabling retrieval-augmented generation and efficient long-term memory for LLMs by clustering similar topics and precise browsing.
Long-Term Memory with RAG: As Simple as Possible with GPTs & RAG8:04
Explore the simplest way to implement long-term memory with RAG inside ChatGPT by building a GPT trained on uploaded documents, using embeddings and a vector database for fast search.
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
Learn three ways to monetize GPTs: OpenAI revenue sharing, generating leads for your business, and upselling through GPT-powered channels, using funnels with ads, content, affiliates, and outreach.
First: You need a Builder Profile to generate Leads from GPTs8:31
Create a builder profile to generate leads from GPTs by configuring your name, domain, and links, verifying your website, and connecting social profiles to attract ad revenue and upsells.
Create a GPT with Knowledge that can generate Leads and makes Upsells20:49
Create a knowledge-driven GPT that generates leads and upsells by training on PDFs and uploaded knowledge, delivering clear instructions and routing users to your web page or YouTube channel.
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
Master integrating every api into your GPT by obtaining api keys, formatting an OpenAI schema, and testing actions to create a high-performing, seo-friendly GPT.
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
Compare open-source LLMs, which you can download, run locally, and fine-tune for free, with closed-source LLMs that require paid APIs and standard interfaces like ChatGPT, Gemini, Claude, and Copilot.
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
Discover Google Gemini in the standard interface, including 1.5 with a 2 million token context window, multimodal capabilities, transformer architecture, and nano on-device options for fast local use.
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
Explore Claude by Anthropic and its cloud models, including cloud three opus and cloud three sonnet, and compare with Gemini and GPT-4 while noting multimodal capabilities and API basics.
The Leading Companies Are OpenAI, Google & Anthropic: Many Are Building on Them2:28
OpenAI and Google's Gemini lead the field, with Anthropic behind; Perplexity and Poe showcase practical tools for ChatGPT-driven workflows.
Perplexity: Advantages and Disadvantages, and Applications6:12
Explore perplexity as a fast AI-powered search tool that uses the latest models, provides concise web results with many sources, and offers multimodal capabilities with Dall-E 3 and stable diffusion.
Poe, The Versatile All-in-One Platform9:02
Poe offers a versatile all-in-one platform that combines chat assistants, lms options, and diffusion models for text and images, with free access and paid subscriptions, but questions their value.
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
Explore using Microsoft Copilot in the web interface for free, with prompts, vision, microphone input, plugins, and cross‑app integration in Word, Excel, and PowerPoint.
Microsoft Copilot PCs3:55
Explore how Microsoft Copilot on PCs with an NPU chip runs locally, enabling recall, file search, real-time translations, and AI-assisted tasks while preserving privacy offline.
Microsoft 365: Differences Between Free and Paid Subscription11:58
Explore how Microsoft 365 offers a free web interface with Copilot across Word, Excel, and more, and compare it to paid personal, family, and business plans with desktop apps.
The Right Copilot Subscription and a Free Alternative.16:41
Identify the right Microsoft Copilot subscription for personal, family, or business, compare Copilot Pro versus business, and discover a free GPT for Word and Excel add-in as an alternative.
Copilot in Microsoft Word: Write Faster Than Ever16:15
Explore how to use Copilot in Word with Microsoft 365, including Copilot Pro, web and local interfaces, and a larger context window, to draft, format, and summarize documents.
Copilot in Microsoft PowerPoint: The Quick Presentation6:17
Harness Microsoft Copilot in PowerPoint to generate an entire presentation from a single prompt, using a zero-shot prompt to create slides about dog food and dog health.
Copilot in Microsoft Outlook: Write and Reply to Your Emails Faster6:46
Use Microsoft Copilot in Outlook to summarize, translate, answer, and write emails quickly. Learn how Copilot coaches you to craft professional messages and translate between English and German.
Copilot in Microsoft Excel: Big Possibilities but Still a Bit Early7:51
Discover how Excel Copilot enables ai-powered data analysis with features like formula columns, highlights, sort and filter, and insights from pivot tables.
Microsoft Copilot GPT: Create your own personalized ChatBots10:19
Learn to create your own Copilot GPT inside Microsoft Copilot, tailoring it with tools like designer, vacation planner, or fitness trainer, using Dall-E and web browsing.
GitHub Copilot: The AI Solution for Programmers5:25
Explore GitHub Copilot, the AI code assistant on GitHub that helps programmers generate code and chat with prompts. Learn about pricing, extensions, and real-world usage.
Conclusion on Microsoft Copilot2:49
Evaluate the value of Microsoft Copilot within Microsoft 365 and weigh subscription costs against productivity gains. Compare Copilot Studios pricing with API-based workflows using tools like Lang Chain.
Review of the Closed-Source LLMs3:50
This lecture reviews closed-source llms, noting that training data and fine tuning matter beyond token limits. It surveys Gemini, Copilot, Notebook LM, Cloth, Perplexity, and GitHub Copilot, and discusses subscriptions.

What Is This About? APIs of Closed-Source LLMs1:06
Explore closed-source APIs with an OpenAI API overview, playground, and billing setup, and grasp how API calls return responses to choose the right LM for your app.
Overview of the OpenAI API6:01
Explore how the OpenAI API enables access to text generation, embeddings, image and speech tasks through Python or other environments, with per-token pricing, the playground, and the assistant API.
Pricing Models of the OpenAI API8:33
Demystify OpenAI API pricing, from per-token costs to model tiers like GPT-4 Omni and GPT-3.5 Turbo, and cover embeddings, fine-tuning, code interpreter, DALL-E, whisper, and playground pricing.
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
discover the OpenAI playground with a billing account, test chat and completion models, adjust temperature and tokens, and explore code, functions, and API keys for building apps.
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
Compare Anthropic's Claude models, including haiku, sonnet, and opus, with OpenAI and Gemini, noting opus is expensive and Anthropic's API is not yet competitive.
Summary of the Closed-Source APIs3:31
Explore closed-source APIs—OpenAI, Gemini, Google, and Anthropic—set up a billing account and API key, test in the playground, compare pricing, and preview open-source, local options.

What Are Open-Source LLMs and Which Ones Are Available4:36
Explore open source large language models, learn to run them locally or in the cloud for free, fine-tune uncensored models, and integrate them into your applications using Hugging Face.
Huggingface: An Introduction6:50
Explore Hugging Face, a web platform for open source machine learning and NLP that hosts models, datasets, spaces, and tutorials, with API keys and cloud or local deployment.
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
Grok offers an lpu-based interface that replaces gpus for ultra-fast inference on models like llama, gem, and mistral, with real-time performance and private, local, uncensored execution via the grok api.
Installation of LM Studio for Opensource LLMs: You need GPU, CPU, Cuda, Ram6:19
Explore open source lms options and install LM Studio to run models locally, detailing the required GPU, CPU, CUDA, and RAM to ensure smooth performance.
Using Open-Source Models in LM Studio: Llama3, Mistral; Phi-3 & more14:09
Explore the LM Studio interface to download, install, and run open-source models locally, including Llama3, Mistral, and Phi-3, while comparing censored and uncensored options.
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
Set up a local inference server with LM studio to host your own apps, then run a local http server that mimics OpenAI endpoints for testing the dolphin model.
Ornith3:01
Finetuning an Open-Source Model with Huggingface or Google Colab6:32
Learn to fine-tune open-source models using Hugging Face AutoTrain or Google Colab, prepare a strong data set, and manage GPU costs from free options to thousands of dollars.
What You Should Remember4:00
Explore open source versus closed source models, run and fine-tune them locally or in the cloud with Hugging Face, Grok, or LM Studio, while prioritizing privacy and bias considerations.

What Is This Section About2:20
Explore making API calls with the OpenAI API in Google Colab, using copyable code and a ready-to-run notebook to generate text, images, speech, transcripts, and vision features with low costs.
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
Learn how Google Colab provides cloud GPU/CPU/TPU power to run code in notebooks, install packages, and execute code from ChatGPT or GitHub for interactive Python projects.
What You Will Create in Google Colab3:33
Install the OpenAI package in Google Colab and run omni chat, DALL-E image generation, and speech-to-text workflows. Configure API keys and explore the vision API inside the notebook.
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
Learn how to generate images with Dall-E 3 via the OpenAI API in Google Colab, including setup in Python, API key configuration, prompts, and displaying or downloading results.
Text-to-Speech (TTS) with the OpenAI API in Google Colab9:23
Learn to implement text-to-speech via the OpenAI API in Google Colab, using TS-1 and TS-1 HD, with code blocks, quick start, and saving audio files.
Transcribing with Whisper via the OpenAI API in Google Colab5:17
Learn to transcribe audio with open source whisper via the OpenAI API in Google Colab, including setting API keys, uploading MP3s, and generating fast, low-cost transcripts.
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
Learn how to use OpenAI models in Google Colab and apps via api keys, copy a few lines of code, and build affordable, standalone AI apps and web integrations.

AI Agents: Definition and Available Tools for Creating Apps and Helpers14:10
Explore ai agents defined as a supervisor LM coordinating sub-experts to perform tasks, using platforms like Bot Press, Lang Cheyne, Lang Flow, Flow wise, and Vector Shift to build chatbots.
The Vectorshift Interface for AI Agents and AI Apps10:18
Explore vector shift, a cloud-based, code-free platform built on LangChain for creating ai pipelines and chatbots with a drag-and-drop interface, featuring vector databases and knowledge bases.
The easy way to make a ChatBot in Vectorshift7:46
Build a simple three-node vectorshift pipeline to connect input and output, integrate an lm with an openai node, test it, and deploy as a chatbot.
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
Build an ai agent in Vector Shift that uses a classification lm to route queries to csv-trained or pdf fine-tuning experts across dual vector databases.
Langchain, Langflow, CrewAI, Autogen.... What Do I Need and is Python important?3:07
Explore open-source lang chain ecosystems—lang graph, lang flow, and flow wise—to build AI agents and apps with minimal code, Python or drag-and-drop interfaces, hosted locally or in the cloud.
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
Install flow wise locally with Node.js, start the local flow wise server, and keep it updated with npm update -g flow wise for a live localhost:3000 environment.
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
Build a rug chatbot inside flow wise using a conversational retrieval Q&A chain with pinecone vector store, OpenAI embeddings, a text splitter, and memory options.
Function Calling, Memory & RAG: Simplified with the OpenAI Assistant API19:25
Build a React chatbot with function calling and internet access using the OpenAI assistant API, LangChain, and a vector store; equip it with tools like a calculator and code interpreter.
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
Build AI agents with a supervisor coordinating two workers using Flowise, Langchain and LangGraph or Autogen and CrewAI, and GPT-4 Omni to design, code, and document a snake game.
Langchain-Style AI Agent with different Experts for Social Media, Math and Code15:11
Explore a Langchain-style ai agent framework with a supervisor and specialized workers—researcher, storyteller, YouTube and Twitter experts, and a title generator—connected by tools and web access for social content.
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
Discover how to host a chatbot in the cloud using render, including forking a GitHub repo, deploying a web service, and configuring environment variables and disk options for client use.
Embed a Chatbot into Websites: HTML, WordPress, Shopify Page & More10:03
Learn to deploy and embed chatbots on websites using HTML, JavaScript, and WordPress or Shopify pages, with customizable visuals, public hosting, and seamless web page integration.
Flowise Tips: Getting Leads, API Endpoints, Speech Recognition & More12:20
Explore Flowise tips to improve your chatbot, including stats on user interactions, chat feedback, starter prompts, rate limits, speech-to-text, leads capture, and API endpoints.
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
Explore insanely fast local inference using the Grok API to accelerate chat models like llama variants, achieving about 1000 tokens per second and easy API integration.
Check Out the Marketplace: If Else Chain and More2:52
Explore the marketplace to learn how if-else chains and LM chains use prompt templates with OpenAI models for sentiment analysis, routing responses, and integrating file, vector, and image tools.
Overview of Microsoft Autogen, CrewAI and Agency Swarm on Github4:03
Explore autogen, crew ai, and agency swarm on GitHub via docs and tutorials to judge practicality, while noting Lang Chain with lang graph and Lang Flow guide current development.
Review and What You Should Do4:02
Learn to design and deploy AI agents and chatbots using vector shift, Lang Chain, Lang Graph, and flow wise, with local and cloud hosting and function calling.

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: