Ben Popper chats with Keith Babo, Head of Product at Solo.io, about how the API security landscape is changing in the era of GenAI. They talk through the role of governance in AI, the importance of data protection, and the role API gateways play in enhancing security and functionality. Keith shares his insights on retrieval-augmented generation (RAG) systems, protecting PII, and the necessity of human-in-the-loop AI development.
Retrieval-augmented generation (RAG) is one of the best (and easiest) ways to specialize an LLM over your own data, but successfully applying RAG in practice involves more than just stitching together pretrained models.
Ben and Eira talk with LlamaIndex CEO and cofounder Jerry Liu, along with venture capitalist Jerry Chen, about how the company is making it easier for developers to build LLM apps. They touch on the importance of high-quality training data to improve accuracy and relevance, the role of prompt engineering, the impact of larger context windows, and the challenges of setting up retrieval-augmented generation (RAG).
A look at some of the current thinking around chunking data for retrieval-augmented generation (RAG) systems.
The home team is joined by Michael Foree, Stack Overflow’s director of data science and data platform, and occasional cohost Cassidy Williams, CTO at Contenda, for a conversation about long context windows, retrieval-augmented generation, and how Databricks’ new open LLM could change the game for developers. Plus: How will FTX co-founder Sam Bankman-Fried’s sentence of 25 years in prison reverberate in the blockchain and crypto spaces?
The home team discusses the challenges (hardware and otherwise) of building AI models at scale, why major players like Meta are open-sourcing their AI projects, what Apple’s recent changes mean for developers in the EU, and Perplexity AI’s new approach to search.
Machine learning scientist, author, and LLM developer Maxime Labonne talks with Ben and Ryan about his role as lead machine learning scientist, his contributions to the open-source community, the value of retrieval-augmented generation (RAG), and the process of fine-tuning and unfreezing layers in LLMs. The team talks through various challenges and considerations in implementing GenAI, from data quality to integration.
This is part two of our conversation with Roie Schwaber-Cohen, Staff Developer Advocate at Pinecone, about retrieval-augmented generation (RAG) and why it’s crucial for the success of your AI initiatives.
On this episode: Roie Schwaber-Cohen, Staff Developer Advocate at Pinecone, joins Ben and Ryan to break down what retrieval-augmented generation (RAG) is and why the concept is central to the AI conversation. This is part one of our conversation, so tune in next time for the thrilling conclusion.
Retrieval augmented generation (RAG) is a strategy that helps address both LLM hallucinations and out-of-date training data.
