Good or bad, generative artificial intelligence or Gen AI tools have been in the news. Often they are found ‘hallucinating’ users with their made-up answers. Now, professionals in the Silicon Valley have discovered a trick to reduce AI hallucinations, reports Wired.
What is retrieval augmented generation (RAG)?Called the retrieval augmented generation or RAG, it does not rely on information coded during initial training.Instead, it augments a user’s prompts by gathering information from a custom database and then a large language model (LLM) generates an answer based on that data.
How is this process different from a standard ChatGPT output?
The difference is that RAG can ‘pull in real documents’ to ‘anchor the response of the model to those documents’, Pablo Arredondo, a vice president of CoCounsel at Thomson Reuters, told Wired.
For instance, a company may upload all of its HR policies and benefits to a RAG database and then ask the AI chatbot to focus on answers that can be found in those documents only.
“You’re rewarding it, in the way that you train the model, to try to write something where every factual claim can be attributed back to a source,” Patrick Lewis, an AI modeling lead at Cohere said.
RAG outputs are not hallucination-freeThe report describes the outputs given by RAG as “low hallucination” rather than hallucination-free. It states that various factors play a role in reducing the hallucinations. These could be the quality of the overall RAG implementation, and how you decide to define AI hallucinations
“The process is definitely not some panacea that eliminates every mistake made by AI”, the report states. “Though, exactly how much RAG reduces AI hallucinations is a point of contention for researchers and developers.”
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