Retrieval, rerankers, and RAG tips and tricks | Data Brew | Episode 39

Data Brew by Databricks

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In this episode, Andrew Drozdov, Research Scientist at Databricks, explores how Retrieval Augmented Generation (RAG) enhances AI models by integrating retrieval capabilities for improved response accuracy and relevance.
Highlights include:
- Addressing LLM limitations by injecting relevant external information.
- Optimizing document chunking, embedding, and query generation for RAG.
- Improving retrieval systems with embeddings and fine-tuning techniques.
- Enhancing search results using re-rankers and retrieval diagnostics.
- Applying RAG strategies in enterprise AI for domain-specific improvements.

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