Hallucinations in generative AI models can lead to incorrect predictions, with examples like Microsoft's AI inventing meeting attendees and false conference call subjects.
RAG, or Retrieval Augmented Generation, is a technical approach aiming to eliminate hallucinations in generative AI by ensuring traceability of generated information to credible sources.
Generative AI vendors like Squirro and SiftHub leverage RAG technology to provide personalized responses with zero hallucinations, enhancing transparency and trust in AI.
RAG, developed by data scientist Patrick Lewis, retrieves relevant documents to provide context for generative AI models to generate accurate answers, reducing the risk of hallucinations.
[
add
]
[
|
|
...
]