For starters, it's not just APIs. Like I pointed out with the C example, programming language syntax and semantics also evolve over time. But also, LLMs' use o

For starters, it's not just APIs. Like I pointed out with the C example, programming language syntax and semantics also evolve over time. But also, LLMs' use of RAG to keep track of API evolution is limited. You can see this if you watch an agent at work using a well-known library that has a high rate of breaking changes such as Polars or Guava. There's a huge amount of churn on repeatedly writing code that works with an older version of the API and then diagnosing and fixing the resulting compile- or run-time errors. It can burn through quite a lot of tokens, which drives up usage costs. I agree that, all else being equal, using language model training to bake knowledge that's easy to look up into the system is kind of silly and inefficient. That's actually been one of my top complaints about hawking these LLMs as a sort of general-purpose AI. But the fact of the matter is that's fairly fundamental to how they work, and RAG is arguably just a hack on top of the basic design to paper over this limitation. RAG's limits become pretty easy to see when working in knowledge domains that aren't very publicly accessible, and therefore produce little text that would have been incorporated into the models' training corpora. It can be a bit of a, "Ignore that man behind the curtain!" experience. And no I'm not just talking about local models. I've seen it happen with recent GPT-5 and Claude Opus series models, too.