[1/2] Hands-On RAG for Production (AI column)

I read the available chapters of this booksWhat is in the process of writing. The main idea of the authors Ofer Mendelevitch and Forrest Bao was to talk about the retrieval augmented generation as an engineering system and consider the issues of data reception into the platform, access rights, latency responses, automatic evaluation, privacy and so on. This makes the book very different from others, where RAG looks just another pattern on top of AI, but this is understandable, because the authors work in the same book. VectaraCompanies that make the platform for agents, as well as RAG as a service (Some of the examples in the book are related to this platform.).

In general, the book on the idea of the authors falls into the most painful point of the market: the gap between “demo works” and “the system is experiencing production, audit, increased workload and security issues.” This makes it important not only for engineers, but also for technical managers, not for the sake of the word RAG, but for the engineering discipline around it. (That's probably why I started reading the book.). If you go through the available chapters, the logic of the authors can be traced well: first they collect the base, and then quickly take the reader away from the world of RAG on his knee into the world of restrictions around production. Let's take a closer look at the chapters.

1) "Introduction to Retrieval Augmented Generation (RAG)" That’s basically the foundation: that RAG decides where it’s really better than the naked model, and why it’s so loved in enterprise scenarios. For example, here cars compare RAG and fine tuning models. 2) "Advanced RAG" This chapter begins an interesting discussion of what distinguishes a basic pipeline from a truly useful system. That is, not just dense retrieval, but stronger retrieval stack, reranking, chunking, query rewriting, hybrid circuits and other quality engineering. 3 and 4) "Deploying RAG to Production"The RAG Platform" is the central part of the book. It is here that RAG ceases to be a playful project and becomes a systemic task with its set of constraints and requirements: latency, privacy, explainability, prompt design, compliance, data access, architectural compromises, build-vs-buy. And the fourth chapter of The RAG Platform is particularly good in that it shows that mature RAG almost always turns into a platform layer that starts serving multiple products and teams.

In post-continuation I will share my thoughts on the remaining chapters of the book and tell you who it will be useful for.

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