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Retrieval-augmented generation is one of the most promising techniques for enabling AI models to improve their data and alleviate hallucinations.
Generative AI (GenAI) and large language models (LLM) are undeniably the hottest tech of 2023, and this momentum doesn’t look like it’ll slow in 2024 or beyond. Businesses will continue to invest billions in the technologies, and wealthier organizations will indulge in M&A sprees to make sure they are at the forefront of innovation.
As a business tool, GenAI makes perfect sense — it can make employees more effective and efficient, increase understanding and skills, and open up new opportunities. The danger of increasing organizational reliance on AI is that you need to trust its ability to make the right decision. Without this, the organization could spend a large part of its AI investment double- and triple-checking every prompt and answer to ensure they’re trustworthy. Additionally, AIs can easily fall victim to hallucinations that confuse organizations or set them on entirely the wrong path.
LLMs are probabilistic engines that analyze the input and the available data, and then calculate which words (or sequences of words) should come next in a reply. This is a double-edged approach. It allows organizations to answer potentially any query on any subject and to do so using natural, understandable and grammatically correct language.
However, ultimately LLMs are playing the odds. If their education — and the datasets they learn on and use — can’t match a query, then their only option is to bluff. The answer will look accurate and be delivered with complete confidence, but it isn’t based on reality or any learned knowledge that can add context. For organizations that need to make business decisions and follow best practices based on factual evidence, this greatly reduces AI’s trustworthiness, and so its effectiveness.
thenewstack.io
Generative AI (GenAI) and large language models (LLM) are undeniably the hottest tech of 2023, and this momentum doesn’t look like it’ll slow in 2024 or beyond. Businesses will continue to invest billions in the technologies, and wealthier organizations will indulge in M&A sprees to make sure they are at the forefront of innovation.
As a business tool, GenAI makes perfect sense — it can make employees more effective and efficient, increase understanding and skills, and open up new opportunities. The danger of increasing organizational reliance on AI is that you need to trust its ability to make the right decision. Without this, the organization could spend a large part of its AI investment double- and triple-checking every prompt and answer to ensure they’re trustworthy. Additionally, AIs can easily fall victim to hallucinations that confuse organizations or set them on entirely the wrong path.
LLMs are probabilistic engines that analyze the input and the available data, and then calculate which words (or sequences of words) should come next in a reply. This is a double-edged approach. It allows organizations to answer potentially any query on any subject and to do so using natural, understandable and grammatically correct language.
However, ultimately LLMs are playing the odds. If their education — and the datasets they learn on and use — can’t match a query, then their only option is to bluff. The answer will look accurate and be delivered with complete confidence, but it isn’t based on reality or any learned knowledge that can add context. For organizations that need to make business decisions and follow best practices based on factual evidence, this greatly reduces AI’s trustworthiness, and so its effectiveness.
From RAG to Riches: Dispelling AI Hallucinations
Retrieval-augmented generation is one of the most promising techniques for enabling AI models to improve their data and alleviate hallucinations.
