The advent of large language models (LLMs) enables machine-assisted semantic analysis of text in an unprecedented manner, bringing into reach new research methods for text-based sciences like philosophy. Despite their immense potential, however, LLMs face crucial limitations, such as the dilution of nuanced philosophical content or missing information about texts that have not been included in the training data. Retrieval-Augmented Generation (RAG) systems address these issues by first setting up an explicit corpus of texts, and then, for each research question, retrieving relevant texts before generating responses on this basis. In this way, RAG systems can crucially enhance precision and depth of the results. This talk explores the potential applications of RAG systems in philosophy, their methodological challenges—including semantic search relevance, corpus selection, and granularity—and their technical implementation. Using a RAG system built on the Stanford Encyclopedia of Philosophy, I illustrate both its promise and the complexities of fine-tuning it for reliable philosophical inquiry.