Compiler backend development still heavily relies on manual effort, making it both time-consuming and labor-intensive. While large language models (LLMs) have shown strong capabilities in general code generation, their accuracy in generating backend functions remains limited. Directly using function descriptions as prompt often fails to bridge the gap between function semantics and implementation, resulting in low accuracy. Moreover, improving LLMs’ accuracy on backend functions typically requires fine-tuning, which demands significant computational resources and is impractical to most backend developers. Although several AI-driven approaches for backend generation have emerged, their outputs still require extensive manual modification and remain dependent on fine-tuning LLMs.

In this paper, we propose MultiFork, a retrieval-augmented framework that integrates a multi-modal retriever with LLMs to enhance backend function generation. MultiFork encodes backend-specific attributes as graphs and combines them with function-level textual features to retrieve similar functions from existing backends. Retrieved functions are then used to construct few-shot prompts that guide LLMs in generating accurate target functions without requiring LLM fine-tuning. Experimental results show that MultiFork significantly improves function generation accuracy across six LLMs, and all of them outperform a fine-tuned language model when combined with MultiFork. Moreover, when combined with LLMs, MultiFork improves the accuracy of an existing AI-driven backend generation approach by up to 39.31% in terms of correct statements, further improving backend development efficiency. The artifact for this work is anonymously archived at \href{https://zenodo.org/records/16876827}{Zenodo}.