Insights on RAG by Startup Founders and Researchers
If you’re a heavy user of ChatGPT, you may see the many limitations of AI products today. This leads to a solution called Retrieval-Augmented Generation (RAG), which is drawing more and more attention in Silicon Valley.
In this video, I’m gonna introduce what RAG is and what researchers and startup founders say about its future.
Introduction to RAG:
RAG is a solution to address limitations of current AI products like hallucinations, outdated information, and lack of domain knowledge.
It was introduced by Facebook AI Research in October 2020 to improve performance on NLP tasks.
How RAG works:
RAG incorporates additional information into pre-trained large language models.
It retrieves relevant documents based on user input and uses them to augment the prompt for the language model.
Potential improvements and variations:
Using vector stores for document storage
Pre-processing documents and user input
Different similarity measures for vector comparison (e.g., dot product, cosine similarity, Euclidean distance)
Industry adoption:
Major tech companies (Google, AWS, IBM, Microsoft, NVIDIA) are providing tools for RAG applications.
RAG is being used in various sectors, including education, customer service, and gaming.
Best use cases for RAG:
High-external-knowledge and low-model-adaptation scenarios
Long-term production applications
Adding domain-specific knowledge to LLMs
Limitations and challenges:
RAG reduces but does not completely solve hallucinations
Improving satisfaction rates requires understanding business logic and proper data cleaning
Debate on RAG's future:
Some argue that longer context windows in LLMs might make RAG obsolete
Many experts believe RAG will complement and be enhanced by longer context windows
Alternative approaches:
Combining RAG with long-context LLMs
Developing intelligent agents that can retrieve and process information more effectively
Exploring Mixture of Experts (MoE) architectures
Conclusion:
RAG is currently the most effective solution for many AI applications
It's likely to remain relevant for the next five years
In the long term, smarter architectures may evolve to outperform RAG
Chapters:
0:00 Introduction
0:32 LLM limitations
0:55 A Simple RAG Example
1:45 Potential Improvements
2:34 Big Tech and RAG
3:07 RAG Applications
3:44 When to use RAG?
4:29 Startup Experiences
5:50 Context Window Length
6:27 RAG vs. Long Context
7:54 Technical Limitation
8:26 Agents and MoE
9:07 Conclusion