Master Laravel & AI:
Build Smart Assistants and Revolutionize How Users Access Information

Course by Vehikl

AI-Driven Laravel

Welcome to "AI-Driven Laravel", where you'll master the integration of AI with Laravel to create a powerful, intelligent documentation assistant. In this course, you'll build "Ask the Docs", a project that allows users to ask questions and receive semantically accurate answers based on Laravel's documentation. Through hands-on lessons, you'll learn how to structure a knowledge base, utilize vector embeddings for AI, craft effective prompts, and implement Retrieval-Augmented Generation (RAG) techniques. By the end, you’ll have the skills to create advanced, AI-driven documentation tools that can be applied across any web development project.

Whether you're a Laravel developer looking to enhance your projects with AI or an AI enthusiast curious about Laravel integration, this course will guide you through the entire process - from building the knowledge base to deploying AI-driven solutions.

Course Outline

Here’s what you’ll find in the course

1. 1

   Overview & Project Introduction

   We will guide you through the process of integrating AI into your Laravel projects to create a powerful system that allows users to ask questions against the Laravel documentation and get semantically accurate answers.

2. 2

   Knowledge Base (Laravel Docs)

   We'll take the Laravel documentation and break it down into smaller, meaningful sections that can be individually processed by our AI model.

3. 3

   Vector Embeddings

   Now that we've chunked our documentation, the next step is to convert these chunks into a format that the AI can work with - this is where vector embeddings come in.

4. 4

   Prompt 101

   A good prompt consists of several elements: Persona, Tone, Task, Context, Condition, Exemplar, and Format. Each of these elements serves a purpose in guiding the AI to generate the desired response.

5. 5

   Chatting with an LLM (OpenAI)

   When interacting with an LLM, there are three key roles: System, Assistant, and User. Each plays a crucial part in the conversation flow.

6. 6

   RAG (Retrieval-Augmented Generation)

   RAG is a powerful technique that combines traditional search with AI generation, allowing our system to retrieve relevant information and generate answers.

7. 7

   Advanced RAG (RAG Fusion)

   RAG Fusion takes the basic principles of RAG to the next level, combining multiple retrieval strategies to enhance accuracy.

8. 8

   Function Calling (Building Tools)

   Function calling in AI allows us to extend the capabilities of our system, enabling it to perform specific tasks dynamically.

The course offers a flexible pricing model with multiple tiers to suit different learning needs. You'll get free access to the introductory chapter, while the full course covers advanced topics like vector embeddings, prompt engineering, and Retrieval-Augmented Generation (RAG). Higher-tier options include access to bonus materials, a GitHub repository, a prompt cheat sheet, and even 1-on-1 personalized support sessions. This model ensures that learners can choose the package that best fits their goals, whether they’re looking for core content or extra resources and guidance.

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