A post on tanay.co.in argues that many of the systems built around LLMs are really temporary "harnesses" — scaffolding designed to cover what the model could not do on its own.
The article walks through the familiar pattern behind those layers, pointing to setup work such as retrieval pipelines, output parsing, OCR, and orchestration frameworks as examples of engineering that exists because the model still has a gap to fill.
It also suggests that those gaps appear to be closing faster than many teams might expect. Longer context windows, structured output support, and newer multimodal capabilities have already made some once-common pieces of AI plumbing far less necessary.
Rather than dismissing those tools altogether, the post makes the case for building them with a short lifespan in mind. The argument is that the best harnesses are the ones cheap enough to discard once the next model release absorbs their job.
Source: tanay.co.in