No Priors: Artificial Intelligence | Technology | Startups

The evolution and promise of RAG architecture with Tengyu Ma from Voyage AI

Jun 6, 2024

Tengyu Ma, an Assistant Professor at Stanford and co-founder of Voyage AI, shares insights from his journey in AI. He discusses the rise of Retrieval-Augmented Generation (RAG) architecture and its efficiency in data retrieval. Tengyu emphasizes how RAG systems are becoming the go-to for enterprises due to their accuracy and cost-effectiveness. He reflects on his transition from academia to groundbreaking work in entrepreneurship and the role of foundational data in AI’s evolution. Expect exciting predictions for the future of AI with RAG!

36:20

Episode guests

Tengyu Ma

AI Summary

Highlights

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

RAG architecture enhances response accuracy by combining retrieval and generation steps in AI systems.

Fine-tuning and domain-specific embeddings optimize large language models for specific data domains effectively.

Deep dives

Tengu Ma's Research Agenda Encompasses Varied Fields in Deep Learning

Tengu Ma's research spans from theoretical understanding of deep learning systems to practical applications like large language models and reinforcement learning. His current focus is on enhancing the efficiency of training large language models and improving reasoning tasks.

Debate on RAG Architecture and Alternative Views

02:54

Efficiency and Iterative Retrieval in Agent Training with Large Language Models

01:13

Future AI Systems will use rag that is simpler and multimodal

01:41

Intro

2min

From Academia to Entrepreneurship in AI

4min

Enhancing AI with Retrieval-Augmented Generation

17min

Optimizing Machine Learning with Fine-Tuning

4min

Building Efficient RAG Systems: Strategies and Future Predictions

3min

Startup vs. Academia: Navigating Innovation

6min

After Tengyu Ma spent years at Stanford researching AI optimization, embedding models, and transformers, he took a break from academia to start Voyage AI which allows enterprise customers to have the most accurate retrieval possible through the most useful foundational data. Tengyu joins Sarah on this week’s episode of No priors to discuss why RAG systems are winning as the dominant architecture in enterprise and the evolution of foundational data that has allowed RAG to flourish. And while fine-tuning is still in the conversation, Tengyu argues that RAG will continue to evolve as the cheapest, quickest, and most accurate system for data retrieval.

They also discuss methods for growing context windows and managing latency budgets, how Tengyu’s research has informed his work at Voyage, and the role academia should play as AI grows as an industry.

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Show Notes:

(0:00) Introduction

(1:59) Key points of Tengyu’s research

(4:28) Academia compared to industry

(6:46) Voyage AI overview

(9:44) Enterprise RAG use cases

(15:23) LLM long-term memory and token limitations

(18:03) Agent chaining and data management

(22:01) Improving enterprise RAG

(25:44) Latency budgets

(27:48) Advice for building RAG systems

(31:06) Learnings as an AI founder

(32:55) The role of academia in AI

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No Priors: Artificial Intelligence | Technology | Startups

The evolution and promise of RAG architecture with Tengyu Ma from Voyage AI

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Tengu Ma's Research Agenda Encompasses Varied Fields in Deep Learning

Evolution of Tengu Ma's Work From Matrix Completion to Transformers and Optimizers

RAG System Overview: Retrieval Augmented Generative Models Enhance Data Relevancy

Fine Tuning and Domain-Specific Embeddings Elevate System Performance

Debate on RAG Architecture and Alternative Views

Efficiency and Iterative Retrieval in Agent Training with Large Language Models

Future AI Systems will use rag that is simpler and multimodal

Remember Everything You Learn from Podcasts