SE Radio 673: Abhinav Kimothi On Retrieval-Augmented Generation Software Engineering Radio - The Podcast For Professional Software Developers podcast

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SE Radio 673: Abhinav Kimothi on Retrieval-Augmented Generation

23d ago 55:55

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In this episode of Software Engineering Radio, Abhinav Kimothi sits down with host Priyanka Raghavan to explore retrieval-augmented generation (RAG), drawing insights from Abhinav's book, A Simple Guide to Retrieval-Augmented Generation.

The conversation begins with an introduction to key concepts, including large language models (LLMs), context windows, RAG, hallucinations, and real-world use cases. They then delve into the essential components and design considerations for building a RAG-enabled system, covering topics such as retrievers, prompt augmentation, indexing pipelines, retrieval strategies, and the generation process.

The discussion also touches on critical aspects like data chunking and the distinctions between open-source and pre-trained models. The episode concludes with a forward-looking perspective on the future of RAG and its evolving role in the industry.

Brought to you by IEEE Computer Society and IEEE Software magazine.

… continue reading

1033 episodes

Artwork

SE Radio 673: Abhinav Kimothi on Retrieval-Augmented Generation

Software Engineering Radio - the podcast for professional software developers

60,485 subscribers

published 23d ago

Share

MP3•Episode home

Content provided by SE-Radio Team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by SE-Radio Team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

In this episode of Software Engineering Radio, Abhinav Kimothi sits down with host Priyanka Raghavan to explore retrieval-augmented generation (RAG), drawing insights from Abhinav's book, A Simple Guide to Retrieval-Augmented Generation.

The conversation begins with an introduction to key concepts, including large language models (LLMs), context windows, RAG, hallucinations, and real-world use cases. They then delve into the essential components and design considerations for building a RAG-enabled system, covering topics such as retrievers, prompt augmentation, indexing pipelines, retrieval strategies, and the generation process.

The discussion also touches on critical aspects like data chunking and the distinctions between open-source and pre-trained models. The episode concludes with a forward-looking perspective on the future of RAG and its evolving role in the industry.

Brought to you by IEEE Computer Society and IEEE Software magazine.

… continue reading

1033 episodes

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