When we interact with developer communities, it’s striking how differently people experience AI. As with everything in life, it ultimately comes down to training: you soon learn about prompts, context, models, and so on. Two factors, however, shape that first impression more than anything else: the language you write in and the codebase you point the model at.
This is about using foundation models for code. I’ll touch on benchmarks, but it’s not a deep dive into machine learning.
Because code generation is essentially “predict the next token” natural and programming languages with sparse training data fare worse. Outcomes deteriorate further when the prompt language and the target language mismatch: for instance, generating F# from a Chinese prompt performs far worse than generating Java from the same prompt, which itself trails behind Java generated from an English prompt.[1]
The fastest route to an AI-ready repo isn’t buying “the best” model or tool. It’s understanding how the model will interact with your resources.
The Training Data Dominance Effect
Large language models have favorites. Their performance mirrors their training, which is heavily skewed toward a handful of paradigms, languages, and patterns mined from public repos predominantly code that is:
- Written in English
- Imperative in style
- Object-oriented
- Using Python, JavaScript, or Java
SWE-PolyBench: A multi-language benchmark for repository-level evaluation of coding agents [6]
Step outside and the model penalizes you. Studies show:
- Switching the programming language can cut efficiency by over 20% under the same prompt.
- Switching the human language rewriting the prompt in Chinese instead of English decrease by at least 13%.
Exploring Multi-Lingual Bias of Large Code Models in Code Generation [1]
The Benefits of Following the Crowd
This bias extends to everyday frameworks and libraries. Agents perform better with mainstream tooling simply because they encounter it more often [1]. A model can emit sharp code for React, Express, or Django. Ask for Ramda, Preact, or F#, and the output while syntactically correct lags far behind a skilled human’s effort [2 3].
Clean Code Isn’t Just for Humans
The quality of the code you already have directly affects AI output. Clear naming and solid docs hand the agent richer context every time it scans your repo.
Compare two function declarations
calculateTotalPrice(items, taxRate)
calc(arr, x)
The first, with explicit intent and nouns, is far easier for a model to reason about.
Documentation and comments
LLMs love comment blocks and it’s no accident. They learned that well-commented code signals quality [4]. I’m no fan of comment clutter either, yet the data are clear: docstrings and meaningful names consistently raise output quality.
Reinforcement of Stereotypes
Uneven training data introduces flavor-of-language “stereotypes.” A model saturated with JavaScript inevitably excels at UI logic; one steeped in Python gravitates to data-science snippets. Typical niches include:
- JavaScript: Front-end logic, UI components, Node.js tooling
- Python: Data science, scripting, ML notebooks
- Java: Enterprise-scale, strongly structured systems
Leveling the Field with RAG
Retrieval-Augmented Generation (RAG) feeds fresh, domain-specific context at query time. For code, that means injecting docs, examples, and best practices you choose regardless of mainstream popularity. Research shows RAG can boost success rates by 13.5%, especially for frameworks released after a model’s training cutoff [2].
RAG isn’t a magic wand. Context windows are finite and expensive; feeding more context shifts the bottleneck from technical to financial.
Conclusion
Model performance varies more than most people discuss, and benchmarking remains contentious. If you’re having a rough ride, remember:
- Different models have different language exposure: try another one.
- Stay close to market-standard patterns.
- Keep your repo clear and well documented so your AI agent can shine.
I’d love to hear your thoughts drop any studies or experiences in the comments!
References
- Exploring Multi-Lingual Bias of Large Code Models in Code Generation — https://arxiv.org/abs/2404.19368
- CodeRAG-Bench: Can Retrieval Augment Code Generation? — https://arxiv.org/abs/2406.14497
- Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: A Haskell Case Study — https://dl.acm.org/doi/pdf/10.1145/3650105.3652289
- Impact of AI-Generated Code Tools on Software Readability & Quality — https://arxiv.org/abs/2402.13280
- MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation — https://arxiv.org/pdf/2208.08227
- SWE-PolyBench: A Multi-Language Benchmark for Repository-Level Evaluation of Coding Agents — https://arxiv.org/pdf/2504.08703
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