Retrieval-Augmented Generation (RAG) is frequently marketed as a simple solution for grounding AI responses in real-world data. It sounds straightforward & easy: connect a vector database to a language model, pipe in your data, DONE! But maybe it might be just a little harder than that …
I just saw a blog post from Johann-Peter Hartmann about that topic: RAG mit Mehrwert – Mayflower Blog that I found worth mentioning here. — I’ve attended a talk (or two?) from Johann in the past couple of months about that topic, but now it’s also here as the above blog post.
I like how he outlines the full “architecture” of this GenAI Application and the real value of RAG emerges only when you integrate domain-specific knowledge, context-aware retrieval, and continuous refinement into the system. And this, unfortunately, requires quite a decent amount of software engineering.
I dare say: The effort involved is frequently underestimated. A production-ready GenAI product demands a lot of architecture, testing, and ongoing optimization. And – the one issue that wil likely never go away: You need to think about how to structure and process your input data.
Anyways, I don’t want to summarize the whole article, just check it out: RAG mit Mehrwert – Mayflower Blog
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