No Priors Ep. 52 | With Pinecone CEO Edo Liberty

Accurate, customizable search is one of the most immediate AI use cases for companies and general users. Today on No Priors, Elad and Sarah are joined by Pinecone CEO, Edo Liberty, to talk about how RAG architecture is improving syntax search and making LLMs more available. By using a RAG model Pinecone makes it possible for companies to vectorize their data and query it for the most accurate responses. In this episode, they talk about how Pinecone’s Canopy product is making search more accurate by using larger data sets in a way that is more efficient and cost effective—which was almost impossible before there were serverless options. They also get into how RAG architecture uniformly increases accuracy across the board, how these models can increase “operational sanity” in the dataset for their customers, and hybrid search models that are using keywords and embeds. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EdoLiberty Show Notes: 0:00 Introduction to Edo and Pinecone 2:01 Use cases for Pinecone and RAG models 6:02 Corporate internal uses for syntax search 10:13 Removing the limits of RAG with Canopy 14:02 Hybrid search 16:51 Why keep Pinecone closed source 22:29 Infinite context 23:11 Embeddings and data leakage 25:35 Fine tuning the data set 27:33 What’s next for Pinecone 28:58 Separating reasoning and knowledge in AI

Sarah GuohostEdo LibertyguestElad Gilhost

Feb 22, 202431mWatch on YouTube ↗

EPISODE INFO

Released: February 22, 2024
Duration: 31m
Channel: No Priors
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Accurate, customizable search is one of the most immediate AI use cases for companies and general users. Today on No Priors, Elad and Sarah are joined by Pinecone CEO, Edo Liberty, to talk about how RAG architecture is improving syntax search and making LLMs more available. By using a RAG model Pinecone makes it possible for companies to vectorize their data and query it for the most accurate responses. In this episode, they talk about how Pinecone’s Canopy product is making search more accurate by using larger data sets in a way that is more efficient and cost effective—which was almost impossible before there were serverless options. They also get into how RAG architecture uniformly increases accuracy across the board, how these models can increase “operational sanity” in the dataset for their customers, and hybrid search models that are using keywords and embeds. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EdoLiberty Show Notes: 0:00 Introduction to Edo and Pinecone 2:01 Use cases for Pinecone and RAG models 6:02 Corporate internal uses for syntax search 10:13 Removing the limits of RAG with Canopy 14:02 Hybrid search 16:51 Why keep Pinecone closed source 22:29 Infinite context 23:11 Embeddings and data leakage 25:35 Fine tuning the data set 27:33 What’s next for Pinecone 28:58 Separating reasoning and knowledge in AI

SPEAKERS

Sarah Guo
host
Edo Liberty
guest
Elad Gil
host
Narrator
other

EPISODE SUMMARY

In this episode of No Priors, featuring Sarah Guo and Edo Liberty, No Priors Ep. 52 | With Pinecone CEO Edo Liberty explores pinecone CEO Explains Vector Databases, RAG, And Scalable AI Memory Pinecone CEO Edo Liberty discusses how vector databases provide long-term, scalable “memory” for AI systems by storing and retrieving embeddings rather than raw text or keywords.

RELATED EPISODES