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Build AI-Powered Laravel Apps with RAG, OpenAI & Qdrant
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Build AI-Powered Laravel Apps with RAG, OpenAI & Qdrant

Build AI chatbots, semantic search & RAG apps with Laravel, OpenAI & Qdrant (practical project)
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Created byRajat varshney
Last updated 3/2026
English

What you'll learn

  • Build production-ready AI features in Laravel using OpenAI APIs and embeddings
  • Understand and implement Retrieval-Augmented Generation (RAG) from scratch
  • Integrate Qdrant vector database for efficient semantic search in Laravel apps
  • Store, index, and retrieve embeddings for intelligent search experiences
  • Optimize prompts and responses for better accuracy and performance

Course content

7 sections • 18 lectures • 1h 30m total length

  • Course Introduction: What You’ll Build with AI, Laravel & RAG3:12

Requirements

  • Basic understanding of PHP and Laravel fundamentals (routes, controllers, APIs)
  • Familiarity with REST APIs and JSON responses
  • Basic programming knowledge (variables, loops, functions)
  • A computer with internet access to run Laravel and API integrations
  • No prior AI or machine learning experience required — everything will be taught from scratch

Description

Are you ready to build a smart AI-powered search system in Laravel using modern tools like RAG, OpenAI, and vector databases?

In this course, you’ll learn how to integrate intelligent semantic search into a real-world Laravel application by building a complete, practical project from scratch.

This is not just theory — you will actually build a Smart AI Search system using:

OpenAI APIs (for embeddings and intelligent responses)
Qdrant (vector database for semantic search)
Laravel (backend application)

By the end of this course, you’ll clearly understand how modern AI search systems work — including how platforms deliver context-aware, relevant search results instead of simple keyword matching.

We will start from the basics and gradually move toward implementing a fully functional semantic search system. You’ll learn how to generate embeddings, store them in a vector database, and retrieve the most relevant results using similarity search.

You’ll also implement a RAG (Retrieval-Augmented Generation) pipeline to enhance search results with AI-generated responses.

This course is designed to be practical and beginner-friendly for developers, even if you have no prior experience with AI or machine learning.

By completing this course, you’ll gain the confidence to build and extend smart search systems for blogs, documentation platforms, knowledge bases, and more.

Who this course is for:

  • Laravel developers who want to integrate AI features into real-world applications
  • Backend or full-stack developers curious about building AI-powered apps using OpenAI and RAG
  • Developers looking to learn vector databases like Qdrant for semantic search
  • Engineers who want to build practical projects like Smart AI search

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