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MongoDB + AI: Build Intelligent Apps with Vector Search LLMs
Last updated 12/2025
Duration: 1h 24m | .MP4 1280x720 30fps(r) | AAC, 44100Hz, 2ch | 450.70 MB
Genre: eLearning | Language: English
Build AI-powered apps using MongoDB, vector search, embeddings, and LLM integration-step-by-step, beginner friendly.
What you'll learn
- Learn to store, index, and query vector embeddings in MongoDB for intelligent AI-driven search.
- Build full-stack AI apps using LLMs, vector search, and real-time data pipelines with MongoDB Atlas
- Implement RAG workflows to boost LLM accuracy, reduce hallucinations, and deliver context-aware results.
- Deploy scalable, production-ready AI features with indexing, performance tuning, and secure APIs.
Requirements
- Students should have basic computer skills, beginner-level programming knowledge, and a willingness to learn MongoDB and AI concepts.
Description
Welcome toMongoDB + AI: Build Intelligent Apps with Vector Search & LLMs, a hands-on course designed for developers who want to combine modern NoSQL databases with cutting-edge artificial intelligence. This course takes you from MongoDB fundamentals all the way to building real, production-ready AI-powered applications using embeddings, vector search, and Large Language Models (LLMs).
You'll begin with a solid foundation in MongoDB-documents, collections, indexes, schema design, and performance basics. Once your core skills are ready, we transition into the world ofAI-driven search and retrieval, where you'll learn how embeddings work, how vector similarity search differs from keyword search, and why it's essential for AI apps.
Next, we dive deep intoMongoDB Atlas Vector Search, where you'll implement semantic search, re-ranking pipelines, hybrid search, metadata filtering, and more. You'll work with popular embedding models and learn how to store, index, and query high-dimensional vectors efficiently.
From there, we integrate MongoDB withLLMs like GPT, Claude, and open-source models, building projects such as:
AI-powered Q&A bot using your own documents
Product recommendation engine using vector similarity
Intelligent chatbot with memory stored in MongoDB
RAG (Retrieval-Augmented Generation) pipelines
Everything is taught using clear explanations, real-world examples, and step-by-step coding sessions. By the end of this course, you'll be able to build end-to-end AI features that are smart, scalable, and production-ready.
If you want to upgrade your AI development skills and build intelligent applications powered by MongoDB and modern LLM technology-this course is made for you.
Who this course is for:
- For beginners, developers, and AI learners who want to build real-world intelligent apps using MongoDB and LLMs.
- Ideal for students and developers eager to integrate vector search and AI features into modern applications
More Info
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