Grep is the fastest tool for many things, but for production RAG it cannot search with meaning, different languages or typos like hybrid vector search can. And it won't scale on terabytes of documents across distributed servers if you want to say under 1000ms.
If the data is this large then why not ask the llm to build a query
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all true for a single grep invocation, however a capable LLM can drive grep using similar terms (`grep (red|maroon|scarlet|crimson)`, moving the measure of similarity from embedding/indexing time to inference time. There's benefits to that.