This often results in hallucinations or factually incorrect responses. No business enterprise would like to be associated with a solution that has even a small probability of giving an incorrect response. Secondly? Hallucination examples from data today is a valuable business asset for every enterprise. Stringent regulations such as GDPR? HIPAA? and CCPA are forcing companies to protect personal data. Breaches can lead to severe financial penalties and damage to the company’s reputation and brand. Overall? organizations want to protect their data by keeping it private and not sharing it with everyone on the internet. Below are some examples of hallucinations from popular AI platforms.
Figure
One potential solution that addresses these two saudi arabia whatsapp number data problems is the RAG (retrieval augmented generation) solution. Developed by researchers from Meta/Facebook? RAG provides more accurate and context-sensitive responses. reducing the risk of LLMs offering general or incorrect responses. The RAG architecture is shown below.
Figure : RAG Architecture
Below is an example? from Clearwater’s GenAI solution – Clearwater Intelligent Console (CWIC). This solution is used for the generation of personalized and customized content tailored to resource c level contact individual preferences? interests? or needs in a simple and intuitive manner for the investment management industry. Clearwater Analytics (NYSE: CWAN) is a fintech SaaS company. It is afb directory the leading provider of web-based investment portfolio accounting? reporting? and reconciliation services for institutional investors. For example? if the CWIC user asks LLM on what Helios is? the response is as shown below.
Figure : Prompts in LLM
But in Clearwater’s context? Helios is one of their products for the investment management industry. So? when the knowledge corpus or Clearwater is enabled with RAG? the same question gets a different response? as shown below? along with the citations. Citations give explainability.