The evolution and promise of RAG architecture with Tengyu Ma from Voyage AI
No Priors: Artificial Intelligence | Technology | Startups
Deep Dive
Why did Tengyu Ma decide to start Voyage AI after his academic research?
He felt the timing was right for commercialization as AI technologies had matured, making it easier to apply AI to industry with foundation models. The process of applying AI had become much simpler, requiring only prompt tuning and retrieval-augmented generation (RAG) on top of pre-trained models.
What is the main bottleneck in implementing RAG systems according to Tengyu Ma?
The quality of the retrieval part is the main bottleneck. If the retrieved documents are relevant, the large language model can synthesize good answers, but poor retrieval quality significantly impacts the response quality.
How does Tengyu Ma compare RAG to long-context transformers in terms of cost efficiency?
RAG is much cheaper than long-context transformers because the latter requires storing all intermediate computations for large contexts, which can be prohibitively expensive. RAG, being a hierarchical system, is more cost-efficient as it retrieves only relevant information for each query.
What are the two main ways to improve RAG systems according to Tengyu Ma?
One is to improve the neural networks, such as embedding models and re-rankers, which require heavy data-driven training. The other is to improve the software engineering aspects, like better data chunking, iterative retrieval, and incorporating metadata.
How does domain-specific fine-tuning improve embedding models in RAG systems?
Domain-specific fine-tuning allows embedding models to excel in particular domains by customizing the limited number of parameters to focus on specific tasks. This can lead to improvements of 5% to 20% in retrieval quality, depending on the domain and the amount of data available.
What advice does Tengyu Ma give to companies building RAG systems?
He suggests starting with a prototype and immediately profiling both latency and retrieval quality. If retrieval quality is the bottleneck, companies should consider swapping components like embedding models or re-rankers to improve performance.
What does Tengyu Ma predict for the future of RAG systems as LLMs improve?
He predicts that RAG systems will become simpler, with fewer components and less need for complex software engineering. Embedding models will handle multi-modality and data formats more effectively, reducing the need for manual preprocessing.
What role does Tengyu Ma believe academia should play in the AI industry?
He believes academia should focus on long-term innovations and research questions that industry may not prioritize due to short-term incentives. This includes working on efficiency improvements and challenging reasoning tasks that require innovative approaches.
- Focus on theoretical foundations and practical applications of deep learning.
- Emphasis on efficiency in training large language models.
- Development of the SOFIA optimizer, resulting in significant training efficiency improvements.
Shownotes Transcript