PyConDE & PyData Berlin 2024

Nowadays, Retrieval Augmented Generation (RAG) architecture has become quite the standard approach for building high-quality document search products or personal assistant applications. Prototyping a RAG application might yield quite convincing results from the very first stages of development, but how do you know if it’s really any good when you move your application from prototype into production? And how do you justify the design choices you make? For example, do you know if long-context models would perform better than short-context models with chunking for long-form documents you have at hand? Or, what difference does it make if you keep your different types of documents in one index or in separate ones? Or, is usage of few-shot learning really worth it for your use case, given that adding examples can increase the cost dramatically compared to zero-shot learning? And of course, how do you know there isn’t a better prompt out there for making the LLM do exactly what you expect it to?

At Airbus, we went through this thought process during the development of a RAG-based assistant for creation of assembly manuals - documents which help our colleagues in Manufacturing navigate through the airplane parts construction procedures. For answering these and other questions, we produced an evaluation concept for our Generative AI applications, which relies on different methods and metrics for RAG evaluation end-to-end and testing each of its components separately. In this talk, we will present our evaluation concept, how we implemented it with tools like LangChain and Ragas, what metrics we use and how we conduct our experiments with the help of Google Vertex AI Pipelines.