{"id":77929,"date":"2024-06-05T06:00:00","date_gmt":"2024-06-05T13:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=77929"},"modified":"2025-03-27T15:52:35","modified_gmt":"2025-03-27T22:52:35","slug":"rag-business-impact","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/news\/stories\/rag-business-impact\/","title":{"rendered":"Independent Analyst Firm: Retrieval Augmented Generation Offers a More Efficient Pathway for Trusted Generative AI"},"content":{"rendered":"\n

Editor\u2019s note: Einstein 1 Platform is now the Salesforce Platform. For the latest, go <\/em>here<\/em><\/a>.<\/em><\/p>\n\n\n\n

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A new analysis<\/a> by 451 Research explores why some enterprises are hesitant to leverage traditional generative AI tools and how an approach known as retrieval augmented generation (RAG)<\/a> is emerging to help overcome adoption challenges.<\/p>\n\n\n\n

Why it matters: <\/strong>Most commercially available generative AI tools are based solely on large language models (LLMs), which can sometimes produce hallucinations, reflect the biases of the data they\u2019re trained upon, or lack enough relevant domain knowledge to produce meaningful business insights. Organizations do their best to overcome such issues by \u201cfine-tuning\u201d their LLMs. But doing so can take significant time and money while consuming tremendous amounts of energy.<\/p>\n\n\n\n