Udemy – Vector Database Using LLM With Demo
English | Tutorial | Size: 1.96 GB
Learn to integrate LLMs with vector databases for search, recommendations, and AI-powered applications.
In today’s AI-driven world, Vector Databases and Large Language Models (LLMs) are redefining how data is stored, searched, and understood. This course – “Vector Databases & LLMs: Build AI Systems with Hands-on Demos” – is designed to give you practical, job-ready skills to build intelligent, scalable, and high-performance AI applications for real-world scenarios.
You’ll begin with the foundations of vector databases, exploring how they store high-dimensional embeddings and how they differ from traditional relational systems. You’ll understand why they’re essential for modern semantic search, AI chatbots, recommendation systems, and context-aware applications.
Next, you’ll dive into the integration of LLMs (like GPT and open-source models) with vector databases to create advanced AI-powered systems capable of understanding meaning and intent – not just keywords. You’ll also get hands-on experience with tools such as ChromaDB, Pinecone, and LangChain, performing CRUD operations, building data models, and implementing similarity search and retrieval-augmented generation (RAG) workflows.
The course also covers scaling, optimization, caching, security, and compliance – ensuring your AI systems are production-ready, secure, and reliable.
By the end, you’ll have the expertise to design, build, and deploy semantic search engines, AI assistants, and intelligent data pipelines that seamlessly combine the strengths of LLMs and vector databases for innovative, next-generation AI applications.
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