Retrieval, Rerankers, And RAG Tips And Tricks | Data Brew | Episode 39 Data Brew By Databricks podcast

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

about a year ago 45:22

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Kandungan disediakan oleh Databricks. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Databricks atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

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.

… continue reading

42 episod

#Databricks #Data Analytics #Apache Spark #Delta Lake #Machine Learning #Data Engineering #Artificial Intelligence #Tech #Data Science #Science #Lifestyle #Podcasting Education

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

Data Brew by Databricks

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published about a year ago

Kongsi

MP3•Laman utama episod

Kandungan disediakan oleh Databricks. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Databricks atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

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.

… continue reading

42 episod

#Databricks #Data Analytics #Apache Spark #Delta Lake #Machine Learning #Data Engineering #Artificial Intelligence #Tech #Data Science #Science #Lifestyle #Podcasting Education

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