Building production-ready RAG systems

Webinar on September 10 at 8am PT / 11am ET / 5pm CEST

What to Expect:

  • A deep dive into building, optimizing, and scaling Retrieval Augmented Generation (RAG) systems for real-world applications.

 

What You Will Learn:

  • Data Ingestion & Preprocessing: Learn efficient strategies for handling diverse data sources, optimizing chunk size, and leveraging metadata for improved retrieval.
  • Query Enhancement: Master techniques for understanding user intent, extracting keywords, and decomposing complex queries for better retrieval results.
  • Advanced Retrieval & Reranking: Explore techniques for managing LLM context length, mitigating hallucination, and optimizing retrieval performance for various use cases.
  • Response Synthesis & Prompting: Develop effective prompting strategies, implement guardrails, and optimize for accurate and relevant responses.
  • Performance & Scalability: Learn how to optimize pipelines for efficiency and reduce the number of LLM calls for cost optimization. Discover strategies for parallelization and scaling your RAG system effectively.

 

Who Should Attend:

  • Machine Learning Engineers and Data Scientists working on or interested in RAG systems.
  • AI Practitioners seeking practical insights and solutions for real-world deployment.
  • Product Managers and Tech Leads focused on integrating advanced AI systems into production environments.
  • Anyone passionate about cutting-edge AI technologies and looking to apply these techniques in their projects.

Anish Shah

ML Engineer
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