To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generat

There is a problem with large language models that the benchmark scores tend to obscure. Models trained on vast swaths of public code learn to navigate public libraries with something approaching fluency — they have, after all, read the documentation, the Stack Overflow threads, the ten thousand GitHub repositories where someone solved the same problem in slightly different ways. But point one of these models at a private, proprietary, or simply obscure library and the performance collapses. A paper surfaced on ArXiv on March 20th makes the case for why, and the argument is more precise than the usual "models don't know what they haven't seen." The researchers draw a distinction that sounds obvious once you hear it but carries real weight: seeing is not the same as mastering. A model can have encountered API documentation during training and still fail to use it correctly when the moment comes. The gap between recognition and reliable execution turns out to be wide. The paper — titled "To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation" — proposes methods for closing that gap, though the specific techniques remain to be evaluated by the wider research community. The problem it names, though, is real and felt daily by engineering teams working on internal tooling.

If confirmed, here is what this means. Every organisation running AI-assisted coding tools against internal libraries is quietly living with a version of this failure mode — they just may not have named it this precisely. The implication is that simply feeding documentation to a model at inference time, the kind of retrieval-augmented approach many teams have already reached for, may be insufficient. Reliable use of private APIs might require something closer to targeted fine-tuning or structured exposure during training — which is slower, more expensive, and requires the kind of data hygiene that most organisations have not invested in. For AI coding tool vendors, this is a quiet pressure point: enterprise customers are not using PyTorch, they are using internal SDKs with twelve contributors and no community forum, and the gap between public benchmark performance and real deployment performance is widening right there. The second-order effect is a possible bifurcation in the market between general coding assistants and specialist tools trained on proprietary stacks.

Watch for citations or responses from teams at major AI labs or enterprise coding tool providers — any engagement from practitioners would be the first sign this paper's framing is gaining traction beyond academia. Independent replication of the core finding would change this from an interesting hypothesis to an actionable problem statement.

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