The Modern RAG Stack - by Adam Khakhar - Adam’s Substack
While generative AI and fine tuning foundation models have captured headlines and imagination, RAG has quietly become the critical infrastructure powering practical AI implementations. It's telling that the past 3 Y Combinator batches have been a convention of “RAG for <INSERT INDUSTRY>”. We have seen the shift from Uber for X to ChatGPT for Y and now Agent/RAG for Z.
As an engineer who's built and deployed RAG systems, I've witnessed its evolution from a promising concept to an essential component of modern AI architecture. Fortune 500 companies aren't just experimenting with RAG – they're building their AI strategies around it. This isn't another ephemeral tech trend; RAG addresses fundamental challenges that limit foundation models, which only have general intelligence.
The core challenge is straightforward but critical: how do we transform powerful but generic language models into precise, reliable tools for specific business contexts? While ChatGPT can eloquently explain abstract concepts or generate creative content, businesses need AI systems that understand their specific products, processes, and domain expertise. This is where RAG proves invaluable.
