arXiv:2604.24222v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved strong performance on general code generation, but their effectiveness drops sharply in enterprise settings where software development relies on internal private libraries absent from public pre-training corpora. Existing Retrieval-Augmented Generation (RAG) methods provide a training-free solution by retrieving static API documentation, but our analysis shows that documentation mainly helps models identify what APIs to use and remains insufficient for teaching how to use them correctly. Even w
This paper addresses a critical current limitation of LLMs in enterprise settings, specifically their inability to effectively utilize proprietary knowledge for code generation, indicating active research into practical deployments.
It highlights a significant barrier to LLM adoption in corporate development and proposes a mechanism for 'self-evolving memory' to overcome it, which could unlock substantial productivity gains.
The ability of LLMs to generate functional code within enterprise private libraries moves closer to reality, potentially shifting how companies develop software by leveraging internal, non-public data.
- · Enterprise software development teams
- · Companies with extensive private codebases
- · Developers of AI agents for coding
- · LLM providers focused on enterprise solutions
- · Software development agencies reliant on manual coding for internal tools
- · Companies slow to adopt AI-assisted development tools
- · Entry-level enterprise programmers whose tasks are automated
Enterprises can significantly accelerate their software development cycles for internal applications.
The demand for specialized, custom fine-tuning and retrieval augmentation for LLMs using proprietary data will increase.
This could lead to a 'data moat' effect, where companies with richer internal codebases gain a greater competitive advantage in AI-driven development.
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Read at arXiv cs.AI