Guillaume Laforge is Developer Advocate for Google Cloud. For the past couple years, he has focused on Generative AI, Large Language Models, advanced Retrieval Augmented Generation techniques, and he loves to share his passion with the audience.
Outside of Google, Guillaume wears his Java Champion hat. He is the co-founder of the Apache Groovy programming language. And he's one of the co-hosts of the French tech podcast "Les Cast Codeurs".
Embarking on your Retrieval Augmented Generation journey may seem effortless, but achieving satisfying results often proves challenging. Inaccurate, incomplete, or outdated answers, suboptimal document retrieval, and poor text chunking can quickly dampen your initial enthusiasm.
In this session, we'll leverage LangChain4j to elevate your RAG implementations.
We'll explore:
- Advanced Chunking Strategies: Optimize document segmentation for improved context and relevance.
- Query Refinement Techniques: Expand and compress queries to enhance retrieval accuracy.
- Metadata Filtering: Leverage metadata to pinpoint the most relevant documents.
- Document Reranking: Reorder retrieved documents for optimal result presentation.
- Agentic Approaches: Take advantage of function calling to build more clever agents.
- Data Lifecycle Management: Implement processes to maintain data freshness and relevance.
- Evaluation and Presentation: Assess the effectiveness of your RAG pipeline and deliver results that meet user expectations.
Join us as we transform your simplistic RAG experience from one of frustration to delight your users with meaningful and accurate answers.
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