I recently finished reading “Unlocking Data with Generative AI and RAG” by Keith Bourne, and it provides a comprehensive overview of building systems that can leverage large language models (LLMs) with your own private or new data. This post summarizes my key takeaways from the book.

The core idea is that the key technical components of a Retrieval-Augmented Generation (RAG) system include the embedding model that creates your embeddings, the vector store, the vector search mechanism, and the LLM itself.

Systems thinking is essential for addressing complex challenges: understanding system dynamics is crucial for developing effective and sustainable solutions to environmental, social, and economic problems.