Advanced RAG Course: Build Text to SQL Agentic AI System
Build production agentic AI systems, from architecture to deployment.
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Advanced means you're already in production. RAG works, an agent works, you've hit the weird bugs. What you need now are the patterns that keep a system reliable at scale: evaluation harnesses that catch regressions, multi-agent architectures that actually converge, hybrid search that beats cosine similarity, and observability that points you at the failing step in a trace.
Curated by Param Harrison
Courses at this level skip introductions. They open with a problem you'll recognize and close with a technique you'll use.
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Common questions
Engineers who’ve already shipped RAG, agents, or a production backend and want the next tier. You should be comfortable with Python’s advanced features, have strong API sense, and know what you’re looking to improve.
Very. Expect references to specific algorithms (reciprocal rank fusion, LLM-as-judge eval), specific library internals (LangGraph checkpointing), and production war stories. Not a place to start.
Yes. The Agentic RAG Masterclass pairs well with Hybrid Search Qdrant. LangGraph Multi-Agent Workflows pairs with AutoGen Multi-Agent Systems. Pick the pair that maps to your production use case.