If you want to learn:
- What is the difference between traditional RAG and agentic RAG in AI systems?
- How do AI agents make smarter decisions in retrieval-augmented generation workflows?
- How can you integrate vector databases with n8n for advanced AI retrieval?
- What makes agentic RAG more adaptive than simple RAG pipelines?
- How do you use Supabase with n8n to build sophisticated AI workflows?
- Why should AI agents control the retrieval process instead of following linear workflows?
Then this lecture is for you!
This lecture explores the fundamental differences between traditional RAG and agentic RAG systems, demonstrating how agentic AI transforms static retrieval workflows into adaptive, multi-step processes. You'll discover how traditional RAG follows a linear pipeline—user query to vector-based retrieval to LLM response—while agentic RAG empowers AI agents to make intelligent decisions about retrieval strategies, choosing between vector search, SQL queries, and other tools to find the best context for answering questions.
The lecture covers the agentic approach to retrieval-augmented generation, where large language models control the workflow rather than simply generating responses. You'll learn how AI agents can access vector databases, execute semantic searches using vector similarity, and even perform traditional database queries when appropriate, making the retrieval process smarter and more efficient for complex tasks.
Additionally, this session introduces Supabase integration with n8n workflows, explaining how this PostgreSQL-based database platform stores vector embeddings and supports AI retrieval systems. You'll understand why Supabase is popular for building RAG pipelines, its generous free tier for AI projects, and how to set up your account for storing and querying vector data. The lecture prepares you for building an expert voice agent with complete knowledge base access, combining agentic RAG with automated data pipelines to create sophisticated AI systems that can handle enterprise AI use cases and conversational AI applications.