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It's mind blowing. It's so simple, elegant and... effective! Grep+glob and a lot of iterations is all we need.
We always suspected find+grep+xargs was Turing-complete, and now Claude is proving it.
Grep works great when you have thousands of files on a local filesystem that you can scan in milliseconds. But most enterprise RAG use cases involve millions of documents across distributed systems. Even with 2M token context windows, you can't fit an entire enterprise knowledge base into context. The author acknowledges this briefly ("might still use hybrid search") but then continues arguing RAG is obsolete.
The bigger issue is semantic understanding. Grep does exact keyword matching. If a user searches for "revenue growth drivers" and the document discusses "factors contributing to increased sales," grep returns nothing. This is the vocabulary mismatch problem that embeddings actually solve. The author spent half the article complaining about RAG's limitations with this exact scenario (his $5.1B litigation example), then proposes grep as the solution, which would perform even worse.
Also, the claim that "agentic search" replaces RAG is misleading. Recent research shows agentic RAG systems embed agents INTO the RAG pipeline to improve retrieval, they don't replace chunking and embeddings. LlamaIndex's "agentic retrieval" still uses vector databases and hybrid search, just with smarter routing.
Context windows are impressive, but they're not magic. The article reads like someone who solved a specific problem (code search) and declared victory over a much broader domain.
This glosses over a fundamental scaling problem that undermines the entire argument. The author's main example is Claude Code searching through local codebases with grep and ripgrep, then extrapolates this to claim RAG is dead for all document retrieval. That's a massive logical leap. Grep works great when you have thousands of files on a local filesystem that you can scan in milliseconds. But most enterprise RAG use…
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I’m sure that was your intent but why did you get bogged down talking about code?
hum because Claude Code pioneered the 'grep/glob/read' paradigm, so I felt the need to explain that what works well for coding files can also be applied to more complex documents.
RAG isn't dead, RAG is just fiddly, you need to tune retrieval to the task. Also, grep is a form of RAG, it just doesn't use embeddings.
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How is this an ad? It's a couple thousand words about how they built something complicated that was then obsoleted.
in the same vein that a 'Behind The Scenes Look At The Making of Jurassic Park' is , in fact, an ad. having a company name pitched at you within the first two sentences is a pretty good give away.
This glosses over a fundamental scaling problem that undermines the entire argument. The author's main example is Claude Code searching through local codebases with grep and ripgrep, then extrapolates this to claim RAG is dead for all document retrieval. That's a massive logical leap. Grep works great when you have thousands of files on a local filesystem that you can scan in milliseconds. But most enterprise RAG use…
From there the model can handle 100–200 full docs and jot notes into a markdown file to stay within context. That’s a very different workflow than classic RAG.