Blog: The Emerging Role of LLMs in Domain Code Generation
Why LLMs Matter
Not everyone who writes code is a software engineer. Scientists, statisticians, and geospatial analysts often need to implement code without formal training in programming. LLMs help bridge this gap by turning plain-language prompts into working scripts—making it easier for domain experts to translate their knowledge into computational tasks.
LLMs generally produce code with fewer syntax errors and in a more concise format. This makes the code easier to understand, debug, and maintain—especially beneficial in scientific research where clarity is key.
Given well-structured input, LLMs can adapt to specialized domains. For instance, generating code to compute the Normalized Difference Vegetation Index (NDVI) from multispectral satellite imagery is possible with a carefully designed prompt. This adaptability shows the potential of LLMs in domain-specific applications.
Challenges in Practice
As the complexity of the task increases, the performance of LLMs tends to decline. Vague or incomplete prompts often result in incorrect or overly verbose code. This highlights a key principle: “garbage in, garbage code out.” Clear, specific instructions are essential for accurate code generation.
LLMs may struggle with domain-specific logic or APIs they haven’t been explicitly trained on. This can lead to incorrect assumptions or misuse of domain tools. Without access to the right context, even powerful models can miss the mark.
How to Improve LLM Code Generation
Pairing natural language with mathematical formalism can significantly improve output. Since math is inherently precise, embedding it into prompts helps the model understand the task more clearly and generate more reliable code.
LLM performance improves with iteration. Researchers have found that zero-shot code generation often underperforms in complex scenarios. Instead, they use a step-by-step refinement process, prompting the model repeatedly to guide it toward the correct solution.
Final Thoughts
LLMs are reshaping how we approach coding in scientific domains. They enable faster development, broaden access, and make programming more intuitive for non-experts. But the quality of the output still depends heavily on how we ask the question. With clear instructions and iterative refinement, LLMs can become trusted partners in domain-specific code generation—turning ideas into code, one prompt at a time.
References
Dou, Shihan, et al. "What's Wrong with Your Code Generated by Large Language Models? An Extensive Study." arXiv preprint arXiv:2407.06153 (2024).
Gu, Xiaodong, et al. "On the effectiveness of large language models in domain-specific code generation." ACM Transactions on Software Engineering and Methodology 34.3 (2025): 1-22.
Cao, Zhuchen, et al. "Pragmatic Reasoning improves LLM Code Generation." arXiv preprint arXiv:2502.15835 (2025).
- Written by Theophilus Aidoo
