Personalized Intelligence with RAG
Retrieval-Augmented Generation (RAG) offers a safe and effective way to customize large language models with your own data.
One of the most promising approaches for adapting large language models without extensive pre-training and fine-tuning is Retrieval-Augmented Generation (RAG). Introduced in 2020 by Facebook/Meta AI researchers, RAG enhances the performance and relevance of LLM outputs by effectively and securely using external data sources.
This approach is invaluable for anyone seeking to extract insights from private data. For businesses, this can be a game-changer, offering a competitive edge with proprietary or confidential business data - and with the rising threat of data breaches and cyberattacks, safeguarding sensitive information while leveraging AI is crucial. Consider this detailed example illustrating a customer chatbot at a food delivery company:
RAG Analogy: The AI Librarian
To extend the example: think of an AI assistant as your personal librarian, accessing a secure collection of your chosen documents. The assistant can answer specific questions about your data much more effectively when they have your documents figuratively in front of them, rather than relying solely on their general knowledge. RAG inherently promotes transparency by providing a clear trace of the information used, reducing the risk of fabricated responses or hallucinations. It can also leverage external databases and various tools to ensure comprehensive and up-to-date knowledge retrieval.
Retrieval-Augmented Everything?
At the recent Collision tech conference held here in Toronto, industry leaders recognized the immense potential of enterprise knowledge management systems built with RAG, along with the challenges for its adoption. They emphasized how these systems can enhance user experiences and cut costs through smarter, more personalized customer chatbots, search engines and content generation. Despite the hype surrounding AI, most businesses haven’t yet realized its full potential.
Poor data quality, a longstanding issue for many businesses, negatively affects LLMs’ ability to extract valuable insights, which can lead to inaccurate or misleading outcomes, diminishing user trust and possibly being counterproductive. Additionally, ensuring robust data privacy and overall trustworthiness remain key challenges. Research highlights the importance of preventing systems from being manipulated, whether consciously or unconsciously, to produce unreliable decisions that could be harmful, especially in safety-critical scenarios.
While RAG still currently faces issues with complex 'reasoning' and 'mathematical' tasks, the emergence of multimodal models like ChatGPT-4o, which can process audio, images, and video, signals a shift towards more natural AI interactions that were once just science fiction. These models aim to build a more comprehensive understanding of the real world, leading to unprecedented accuracy and insight in their outputs. This opens up a world of new possibilities, but it also underscores the critical need to address and prioritize data privacy concerns.
It's an exciting, daunting, and potentially transformative prospect, depending on one's perspective on how AI will play out.
Try out RAG
Curious to experience RAG yourself? Here are some ways you can get started:
Upload to regular LLM Chats: Most, if not all, accessible LLMs now have features that resemble RAG, allowing you to upload a few documents at a time and ask questions about them. While this isn't fully-fledged RAG, it provides a practical introduction to the concept. Always remember to check the privacy policy for the model you’re using and ensure it meets your comfort level.
Cohere’s Coral Agent: Cohere’s chat web UI includes RAG-like features, such as specific web search capabilities and file analyzing functions that demonstrate its fetching capabilities from external sources. They also offer great foundational training to learn more about RAG.
You.com: Founded in 2020, You.com started off as a search engine that became the first to integrate consumer LLMs like ChatGPT with real-time Internet access for up-to-date and accurate responses with citations. Without using your own data, it’s a notable example of RAG’s transparency.