Building RAG systems for AI chatbots requires massive engineering investment
8/10 HighRaw GPT models have no knowledge of a company's specific business, products, or policies. Developers must build complex Retrieval-Augmented Generation (RAG) systems to dynamically fetch and feed the right information from help centers, tickets, and documentation in real-time, requiring significant ongoing maintenance.
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The models also lack built-in web search, requiring RAG implementations for current information.
To give useful answers, you have to connect it to your company's knowledge. This process, often called Retrieval-Augmented Generation (RAG), means building a whole system just to find and feed the right information from your help center, past tickets, and other docs to the AI in real-time. This isn't a weekend project; it's a massive engineering investment that needs constant upkeep.