Customize Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.

No cookies to display.

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

Reading Time: < 1 minute

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.

Taylor Swifts New Album Release Health issues from using ACs Boston Marathon 2024 15 Practical Ways To Save Money