Once you get past the chatbot hype, it’s clear that generative AI is a useful tool, providing a way of navigating applications and services using natural language. By tying our large language models (LLMs) to specific data sources, we can avoid the risks that come with using nothing but training data.
While it is possible to fine-tune an LLM on specific data, that can be expensive and time-consuming, and it can also lock you into a specific time frame. If you want accurate, timely responses, you need to use retrieval-augmented generation (RAG) to work with your data.
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