News Summary

• Retrieval-augmented generation (RAG) is one of the most interesting architectural patterns in modern AI applications.
• Instead of relying solely on a model's pre-trained knowledge, RAG combines the language generation capabilities of an LLM with a controlled corporate knowledge base.
• The effectiveness of a RAG pipeline depends not only on the potential of AI models and vector stores, but also on the robustness of the application layer on which the solution is based.
• Spring Boot and Spring AI represent the ideal combination that allows you to orchestrate processes, integrate different LLMs, and maintain a common standard of maintainability and scalability at the same time.