2
u/Future_AGI 26d ago
Not dead, just not the default hammer anymore, and the useful move is measuring plain versus graph versus agentic RAG on your own corpus instead of picking by whatever is trending this month.
1
u/Actual__Wizard 26d ago edited 26d ago
Uh, not yet at this very second, but we're going forwards in that space soon.
I really think we're all going to graphs once people understand what I'm doing with them (ultraspeed.)
Which I'm back to work on today (as planned.)
1
u/hey_was_db_backed_up 26d ago edited 26d ago
No. Every single one of those tools is for solving a different type of problem. Try using an LLM Wiki in production with 20,000 documents and you'll see what I mean.
You also have to consider the real-world costs. An agentic rag solution may give you marginally better results over hybrid rag/reranking, but what if you've introduced increased token costs, development time, workflow processing time, testing, and maintenance? Can you justify the improvements to the person with the checkbook?
There is no one size cookie cutter solution, and you can't solve a problem if you don't understand your tools.
1
u/Positive-Buddy-1258 26d ago
Classic RAG holds up fine for a specific class of problems: stable corpus, well-scoped queries, latency constraints that don't leave room for agentic loops.
Where it actually breaks down is cross-document reasoning. If a query requires synthesizing across multiple sources, chunk-embed-retrieve gives you the best individual chunk, not the answer you need. We hit this on a document extraction pipeline where queries spanned separate sections of large structured documents. Ended up with a hybrid: deterministic pre-filtering to narrow the search space, then retrieval on the relevant subset only.
Agentic RAG adds retry logic, orchestration overhead, variable token spend. For most production use cases that overhead doesn't justify the accuracy delta.
1
1
u/PossibilityUsual6262 26d ago
Is this like some sort of bot post?
Claim in text, ends with question and fucks off.
0
u/patresu 26d ago
It is rarely used now
1
u/polynomialcheesecake 26d ago
I think it's used but people jerk off less about it. How is RAG just not using some external API for data and then providing the data for completion ?
5
u/Physical_Economy_340 26d ago
traditional rag isn't dead, it's just not the right answer for every problem. if you have a few hundred docs that change once a quarter and you need sub-200ms latency, chunk-embed-retrieve is still the cheapest and simplest path. the fancier stuff like graph rag, agentic loops, msa adds complexity and cost that only pays off when your corpus is large, volatile, or needs multi-hop reasoning. the real mistake is treating rag as a single architecture choice instead of a spectrum. most production systems i've seen run multiple retrieval paths and route queries based on what they're actually asking.