No Priors: Artificial Intelligence | Technology | Startups The evolution and promise of RAG architecture with Tengyu Ma from Voyage AI
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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!
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RAG System Overview
- Retrieval Augmented Generation (RAG) retrieves knowledge, like company information, before generating text.
- This reduces hallucinations by giving Large Language Models (LLMs) relevant information as an anchor.
RAG Alternatives
- One alternative to RAG is agent chaining, where LLMs operate on data with instructions (e.g., summarize).
- Another alternative is feeding everything into LLMs with infinite or actively managed context, rather than vectorizing data.
Long Context vs. RAG
- Long-context transformers with proprietary data are currently impractical due to high costs.
- RAG is more cost-efficient, using a hierarchical system akin to computer memory caching.