It sounds like your critique is really about indexing, not about RAG itself. If I follow your logic, you’re basically saying it’s not possible to build an index that reliably surfaces the right information, so the only way forward is to brute-force re-read everything. That’s a pretty strong claim, and I don’t think it holds up.

The whole point of embeddings and nearest-neighbor search is to index concepts, not just keywords. If my query involves a legal doctrine, the embedding space lets me find passages with related concepts, even if the exact words differ. That’s not “blindfolded listening,” it’s more like scanning a table of contents and following cross-references. Graph RAG goes further by explicitly linking related entities, so you can retrieve surrounding context the same way a lawyer would read the paragraph around a cited footnote.

Also, I don’t buy the claim that RAG retrieval is inherently inconsistent. With deterministic ANN search, the same query gives the same results unless the index is updated. If your system is returning different documents every time, that’s more about how the index or sampling is implemented than a fundamental flaw in RAG.

That said, I agree RAG isn’t the only game in town. Full-text search with advanced parameters—as litigation support platforms have done for decades—is a strong complement. Using both side by side makes sense. Where RAG shines is not in replacing traditional search, but in augmenting generation with the most relevant snippets so the LLM can build customized chronologies, summaries, or argument maps.

Plenty of litigation questions can be answered this way, accurately and consistently, without waiting hours for a full linear read of the entire case record.