Agentic RAG Patterns
Combine the power of RAG with agentic reasoning. Build AI systems that dynamically retrieve, reason over, and synthesize information from multiple sources.
Prerequisites
- Experience with RAG systems and vector databases
- Understanding of agent architectures
- Knowledge of embedding models and retrieval strategies
Who Should Attend
- AI/ML Engineers
- Software Developers building AI products
- Technical Leads evaluating agent frameworks
- Senior engineers scaling production systems
- Technical architects designing enterprise solutions
Course Outline
- 1From traditional RAG to Agentic RAG: the evolution
- 2Query planning and decomposition strategies
- 3Multi-source retrieval and source selection agents
- 4Iterative retrieval with agent reasoning loops
- 5Corrective RAG: self-correcting retrieval agents
- 6Routing agents for hybrid retrieval strategies
- 7Combining structured and unstructured data retrieval
- 8Hands-on lab: Build an agentic research assistant
Learning Outcomes
- Design agentic RAG systems for complex queries
- Implement query planning and decomposition
- Build agents that reason over retrieved information
- Create self-correcting retrieval systems
- Combine multiple retrieval strategies dynamically
- Deploy production agentic RAG applications
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