This method combines the strengths of retrieval-based and generative approaches to produce more accurate and informative texts.

The effectiveness of RAG systems depends heavily on the quality and availability of the data they access. In an industrial context, it can be a challenge to ensure that the data used to train and operate the model is up-to-date, relevant and accurate. Incomplete, outdated or irrelevant data can significantly affect the performance of the system.

And this is precisely why the developers at Renumics have published the Industrial AI Canvas .

The idea behind it: More transparency in AI projects and the certainty that levels will not be forgotten. It is important for Suwelack and his team to emphasize that the canvas method is not about planning a project completely in advance, but about gaining an initial overview of opportunities and risks by considering critical key aspects. Throughout the project, the canvas can be used to document the current status and make updates if necessary.

You can hear how the Industrial AI Canvas and RAG interact and how RAG projects are structured in industry in the Industrial AI Podcast.