The Limitations of RAG in Addressing Generative AI’s Hallucination Issue

Hallucinations are becoming a major concern for businesses looking to incorporate generative AI models into their operations. These models, which lack true intelligence and simply predict data based on a private schema, have been known to generate false information. In a recent incident reported by The Wall Street Journal, Microsoft’s generative AI created imaginary meeting attendees and misrepresented the topics discussed during conference calls.

To address this issue, some generative AI vendors are touting a technical solution called retrieval augmented generation (RAG). This approach, pioneered by data scientist Patrick Lewis, involves retrieving relevant documents to provide context for the model to generate accurate responses. Companies like Squirro and SiftHub are using RAG technology to ensure zero hallucinations in the information generated by their AI models.

While RAG has proven to be useful in knowledge-intensive scenarios, such as answering factual questions, it has limitations when it comes to reasoning-intensive tasks like coding and math. Models can still get distracted by irrelevant content in documents or choose to rely on their parametric memory instead of the retrieved information.

Furthermore, implementing RAG can be costly in terms of hardware and compute resources needed to store and process retrieved documents. Despite ongoing efforts to improve the efficiency of RAG, it is clear that this technology is not a foolproof solution to eliminate all hallucinations in AI models.

In conclusion, while RAG can help reduce the occurrence of hallucinations in generative AI models, it is not a perfect solution. Businesses should be cautious of vendors claiming to have completely solved the issue of AI-generated misinformation.