Model context limits are not “artificial” as claimed.
The largest context window a model can offer at a given quality level depends on the context size the model was pretrained with as well as specific fine tuning techniques.
It’s not simply a matter of considering increased costs.
Context extension methods exist and work. Please educate yourself about these rather than confidentially saying wrong things.
Not sure what you’re disagreeing with? Context window size limits are not artificial. It takes real time/money/resources to increase them.
There are a few ways to approach the problem. Pre-training on longer context lengths I’ve already mentioned. Fine-tuning techniques (like LongRoPE) I’ve already mentioned.
Inference time context extension tricks I didn’t mention because the papers I’ve seen seem to suggest there’s often problems with quality or unfavorable tradeoffs.
There’s no magic way around these limits, it’s a real engineering problem.