What you’ll learn
- Implement end-to-end RAG pipelines in Python
- Deploy and tune vector databases like Weaviate
- Apply advanced retrieval techniques
- Orchestrate multi-agent RAG workflows
- Design custom re-ranking strategies
Mastering RAG is like building an application once you’ve grasped a programming language!
Have you ever wondered how to build powerful AI applications that retrieve the perfect data every time? Want to master the technology behind ChatGPT’s data fetching capabilities? Then you’ve come to the right place! Welcome to your ultimate guide to Retrieval-Augmented Generation (RAG), where you’ll journey from the basics of Vector Databases to crafting advanced AI-driven pipelines.
In this hands-on, step-by-step course, you’ll first get comfortable with the core concepts of vector databases—understanding precisely how they store, index, and retrieve data efficiently. Then, you’ll learn how to select the ideal vector database for your specific project, including practical guidance on self-hosting Weaviate.
We won’t stop there. You’ll build simple yet powerful RAG implementations in code and even automate your pipelines using n8n. Once confident, you’ll dive into advanced topics such as image-based retrieval, AI-driven image comparisons, and deploying a team of coding-assistant AI agents leveraging website-based knowledge.
Excited yet? You’ll also discover cutting-edge optimization techniques like Query Expansion, Synonym Expansion, Prompt Caching, Index Optimization, Contextual Re-ranking, and Multi-signal Result Re-ranking.
If you’re ready to level up your AI development skills, click play on our free trial video and peek inside. Are you ready to start your journey?
Who this course is for:
- Software developers & engineers
- Data scientists & ML practitioners
- AI enthusiasts & technical leads
- Product managers & architects
- Anyone curious about teaching themselves the upcoming technology
