{"id":195887,"date":"2025-03-17T14:33:03","date_gmt":"2025-03-17T19:33:03","guid":{"rendered":"https:\/\/narcolepticnerd.com\/2025\/03\/17\/vertex-ai-rag-engine-a-developers-tool\/"},"modified":"2025-03-17T14:33:03","modified_gmt":"2025-03-17T19:33:03","slug":"vertex-ai-rag-engine-a-developers-tool","status":"publish","type":"post","link":"https:\/\/narcolepticnerd.com\/2025\/03\/17\/vertex-ai-rag-engine-a-developers-tool\/","title":{"rendered":"Vertex AI RAG Engine: A developers tool"},"content":{"rendered":"

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Generative AI and Large Language Models (LLMs) are transforming industries, but two key challenges can hinder enterprise adoption: hallucinations (generating incorrect or nonsensical information) and limited knowledge beyond their training data. Retrieval Augmented Generation (RAG) and grounding offer solutions by connecting LLMs to external data sources, enabling them to access up-to-date information and generate more factual and relevant responses.<\/p>\n

This post explores Vertex AI RAG Engine and how it empowers software and AI developers to build robust, grounded generative AI applications.<\/p>\n


What is RAG and why do you need it?<\/b><\/h3>\n

RAG retrieves relevant information from a knowledge base and feeds it to an LLM, allowing it to generate more accurate and informed responses. This contrasts with relying solely on the LLM’s pre-trained knowledge, which can be outdated or incomplete. RAG is essential for building enterprise-grade Gen AI applications that require:<\/p>\n