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RAG · · ·

  • No, RAG Is NOT Dead!

    Posted on
    5 Minutes

    RAG Is Dead?! Long Live Real AI!

    On Friday, April 11, 2025, I hosted a space titled “Is RAG Dead?” with my friend Skyler Payne . Skylar is ex-Google, ex-LinkedIn, currently building cutting-edge systems at Wicked Data and helping enterprises bring real AI solutions to production. This space has been on my mind for quite sometime now. There is this opinion floating around that “LLMs have 1M+ token context length now! We don’t need RAG anymore!” This isn’t just a bad take. It’s a misunderstanding of how actual AI systems are built. So, Skylar and I decided to host this space and talk it out together. What followed was one of the most technical, honest, no-bullshit convos I’ve had in a while about the actual role of RAG in 2025 and beyond.

    If you don’t know me, I’m the guy with the 14x RTX 3090s Basement AI Server . I’ve been building and breaking AI systems for quite sometime now, I hold dual degrees in Computer Science and Data Science. I am also running a home lab that looks like it’s trying to outcompute a small startup (because it kind of is). I go deep on LLMs, inference optimization, agentic workflows, and all the weird edge cases that don’t make it into the marketing decks.

    Let’s break it down.

    Skyler opened by asking the obvious but important question: when people say “RAG,” what do they even mean?

    Because half the time, it’s being reduced to just vector search. Chunk some PDFs, some cosine similarity, call it a day. But that’s not RAG.

    Real RAG, at least the kind that works at scale, is more than just a search bar duct-taped to an LLM:

    Also, definitions matter. If your definition stops at vector search and mine includes multi-hop planning and agents, we’re not debating. Understanding that nuance is key before you even ask whether RAG is “dead.” Because until we agree on the same definition, we’re just yelling at each other for the wrong thing.

    The main argument against RAG is that LLMs can now eat entire PDFs. “Why retrieve when you can just feed it all in?” Doesn’t work.

    Skyler walked us through what entreprises have on their hands: