Understanding AI Hallucinations and Retrieval Augmented Generation
The Reality of AI Hallucinations
If you’ve ever interacted with a generative artificial intelligence tool, chances are it has provided you with incorrect information multiple times. These recurring inaccuracies are often referred to as “hallucinations.”
Introducing Retrieval Augmented Generation (RAG)
One of the most promising methods to reduce AI hallucinations is called Retrieval Augmented Generation (RAG). This approach is gaining traction in Silicon Valley for its potential to make AI tools more reliable.
How RAG Works
The RAG process is intricate, but at its core, it enhances AI by allowing it to generate answers based on specific, pre-uploaded data. For instance, a company could upload all its HR policies and benefits into a RAG database, enabling the AI chatbot to provide answers strictly from those documents.
Comparing RAG to Standard AI
Unlike standard AI, which might pull information from a broad and often unreliable range of sources, RAG narrows the focus to a specific dataset. For example, a publication like WIRED could upload all its print magazines and web articles since 1993 into a private database. A RAG-based AI would then reference these documents when answering reader questions, making it more adept at providing accurate information related to WIRED.
Applications in Various Fields
Legal Sector
In the legal field, a RAG system tailored to legal issues is more effective at answering questions on case law than general-purpose AI like OpenAI’s ChatGPT or Google’s Gemini. However, it can still miss finer details and make random mistakes. AI experts emphasize the need for human oversight to double-check citations and verify the accuracy of results.
Broader Professional Use
The potential of RAG extends beyond the legal sector. According to Arredondo, “Take any profession or any business. You need to get answers that are anchored on real documents. So, I think RAG is going to become the staple that is used across basically every professional application, at least in the near to mid-term.” Risk-averse executives are particularly interested in using AI tools to understand proprietary data without exposing sensitive information to public chatbots.
The Importance of Human Oversight
Despite the advancements in RAG, users must understand the limitations of these tools. AI-focused companies should avoid overpromising the accuracy of their answers. Users should maintain a healthy skepticism towards AI-generated responses, even those improved through RAG.
“Hallucinations are here to stay,” says Ho. “We do not yet have ready ways to really eliminate hallucinations.”
Even when RAG reduces errors, human judgment remains crucial. And that’s no lie.
2 Comments
Isn’t it naive to think that a “simple” software solution can really address the complex issue of AI errors?!
Wisp: Haven’t we heard this promise before?