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Build AI-Powered Apps
Getting Started (7m)
1- Welcome (1:26)
2- Prerequisites (0:55)
3- What You'll Learn (3:54)
4- Setting Up Your Development Environment (0:56)
5- Source Code
6- Questions and Support
7- Connect with Me
Introduction to AI Models (32m)
1- Introduction (0:36)
2- Rise of AI Engineering (4:01)
3- What are Large Language Models? (4:22)
4- What Can You Do With Language Models? (2:25)
5- Understanding Tokens and Context Window (3:02)
6 - Counting Tokens (3:43)
7- Choosing the Right Model (5:02)
8- Understanding Model Settings (8:46)
9- Calling Models (7:35)
10- Summary
Setting Up a Modern Full-Stack Project (51m)
1- Introduction (1:12)
2- Setting Up Bun (1:31)
3- Creating the Project Structure (2:48)
4- Creating the Backend (6:38)
5- Managing OpenAI API Key (5:15)
6- Creating the Frontend (2:45)
7- Connecting the Frontend and Backend (5:13)
8- Running Both Apps Together (3:23)
9- Setting Up TailwindCSS (3:35)
10- Setting Up Shadcn (6:30)
11- Formatting Code with Prettier (5:02)
12- Automating Pre-Commit Checks with Husky (6:50)
13- Summary
Project: Building a Chatbot (2h 4m)
1- Introduction (0:29)
2- Building the Backend (0:36)
2.1- Building the Chat API (6:26)
2.2- Testing the API (1:57)
2.3- Managing Conversation State (6:22)
2.4- Input Validation (5:48)
2.5- Error Handling (2:18)
3- Refactoring the Chat API (2:08)
3.1- Extracting Conversation Repository (5:20)
3.2- Extracting Chat Service (6:44)
3.3- Extracting Chat Controller (3:58)
3.4- Extractring Routes (4:51)
4- Building the Frontend (0:19)
4.1- Building the ChatBot Component (7:28)
4.2- Handling Form Submission (9:23)
4.3- Calling the Backend (3:18)
4.4- Rendering the Messages (4:37)
4.5- Styling Messages (4:35)
4.6- Rendering Markdown Text (2:30)
4.7- Adding a Typing Indicator (4:09)
4.8- Auto-Scrolling to the Latest Message (2:20)
4.9- Improving Copy Behaviour (4:47)
4.10- Improving the Look and Feel (8:04)
4.11- Handling Errors (3:28)
5- Refactorings (1:28)
5.1- Extracting TypingIndicator Component (3:49)
5.2- Extracting ChatMessages Component (5:19)
5.3- Extracing ChatInput Component (8:28)
5.4- Recap (2:36)
6- Summary
Prompt Engineering (32m)
1- What is Prompt Engineering (2:12)
2- Anatomy of a Good Prompt (2:26)
3- Providing Context (4:03)
4- Controlling the Output Format (2:32)
5- Providing Examples (2:44)
6- Handling Errors and Edge Cases (1:55)
7- Reducing Hallucinations (3:35)
8- Refining Prompts (4:06)
9- Improving Chatbot Responses (6:00)
10- Adding Sound Effects (2:38)
11- Exercises
12- Summary
Building a Review Summerizer (2h 20m)
1- Introduction (0:50)
2- Setting Up the Database (0:26)
2.1- Setting Up MySQL (2:23)
2.2- Setting Up Prisma (2:46)
2.3- Defining the Prisma Schema (7:38)
2.4- Running Migrations (6:01)
2.5- Refining the Prisma Schema (3:14)
2.6- Populating the Database with Realistic Data (3:28)
3- Building the Backend (0:20)
3.1- Creating the API to Fetch Reviews (7:53)
3.2- Refactoring- Separation of Concerns (7:38)
3.3- Creating an API for Summarizing Reviews (5:27)
3.4- Generating Summaries (3:00)
3.5- Refactoring- Extracting the LLM Logic (8:49)
3.6- Refactoring- Extracting the Prompt (1:49)
3.7- Storing the Summary (7:24)
3.8- Handling Regeneration (2:47)
3.9- Handling Edge Cases (6:32)
3.10- Fetching the Summary (8:13)
4- Building the Frontend (0:20)
4.1- Displaying Reviews (6:35)
4.2- Displaying Star Ratings (4:52)
4.3- Displaying Loading Skeletons (4:38)
4.4- Handling Errors (2:30)
4.5- Introducing Tanstack Query (7:34)
4.6- Displaying the Summary (2:42)
4.7- Triggering Summary Generation (4:18)
4.8- Displaying Loading Skeletons (4:17)
4.9- Handling Errors (2:50)
4.10- Refactoring with Mutations (4:23)
4.11- Refactoring for Readability (2:44)
4.12- Extracting the API Layer (5:28)
Building with Open Source Models (28m)
1- Introduction (0:22)
2- Why Use Open-Source Models (1:48)
3- Finding Open-Source Models (4:17)
4- Calling Hugging Face Models (5:35)
5- Choosing the Right Model For the Job (6:43)
6- Running Models Locally (3:13)
7- Using Hugging Face Models with Ollama (2:29)
8- Calling Ollama Models (3:03)
Course Wrap Up
1- Course Wrap Up (1:20)
2- Feedback
3- What are Large Language Models?
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