Why AI Coding Assistants Default to Chinese Output and How to Lock Language Behavior

1. Symptom: How the Language Changes Silently

In real usage, language switching is not a single toggle. It spans several independent layers, and these layers are easily mistaken for a model replacement when they occur together:

1. Client interface language: Menus, buttons, and prompt text switch to Chinese.
2. Assistant response language: Explanations, conclusions, and follow-up questions default to Chinese.
3. Process output language: Reasoning steps, planning steps, and task lists change language.
4. Code artifact language: Comments, docstrings, commit messages, test case names, and error explanations change language.

Items 3 and 4 are the most disruptive. Users can directly see and manually adjust the first two, but chain-of-thought style outputs and code comments tend to follow the main response language. If the service decides that the main response language is Chinese, generated artifacts may contain many Chinese comments and Chinese identifiers. This conflicts with an existing English codebase and increases review and internationalization costs.

2. Root Causes: What Decides the Default Language

The output language of an AI coding assistant is not controlled by a single parameter. It is the result of multiple layers of information. The following four causes are the most common.

2.1 Account and Region Inference

Most hosted services perform a language-preference inference before a request reaches the inference layer. This inference may use the request source address, account registration region, timezone, and other signals. The inferred result can then be written into the system prompt as the default language.

This type of inference has several characteristics:

• It is invisible to users: It does not appear in the front-end settings panel, and users cannot find a corresponding switch.
• It changes with the network exit: When the request exit address changes, the inference result may change on the next request.
• It can override historical settings: If the server places the inferred result in a system-level prompt, a user saying “answer in English” in the conversation may only affect the current turn. The next turn may be overridden again.

This explains a typical phenomenon: the user never changed any settings and did not restart the client, but the assistant “woke up” and started speaking Chinese.

2.2 Client Environment Signal Leakage

When establishing a connection, a client often carries a set of environment signals:

Accept-Language: zh-CN,zh;q=0.9
OS locale: zh_CN.UTF-8
Time zone: Asia/Shanghai
Keyboard layout / input method language

These signals are normally used for localization. However, if they are reported together and participate in language decisions, a change in system language may cause a corresponding change in assistant output language. It is worth emphasizing that there is no technical necessity linking the system timezone to the output language. Binding the two is usually an implementation convenience, not a requirement.

2.3 Context Language Distribution

Large language models have a strong tendency to imitate the language that appears nearby. When the proportion of Chinese in the context increases, the probability of continued Chinese output increases significantly. Chinese sources may include:

• User history and pasted Chinese error messages;
• Existing Chinese comments, Chinese README files, and Chinese variable names in the repository;
• Chinese text in requirement documents, issue descriptions, and test cases;
• Localized hints returned by dependency libraries.

This means that even if the default language is fixed to English, output language may still drift if a session contains a large amount of Chinese material.

2.4 Version Upgrades and Default Template Changes

Client auto-updates can update built-in system prompt templates and default configuration items at the same time. A new version may add language-inference logic, adjust the default locale, or introduce a new user-preference field. Because upgrades are silent to users, behavior changes may be perceived as “sudden switching.” In addition, earlier versions may not have had a language option at all, and it may appear only after an upgrade. This is different from “the configuration was changed.”

3. Build a Layered Control Model: Which Language Priority Is Higher?

To control output language reliably, you first need to understand the scope and priority of each layer. A practical layered model is shown below. In general, layers closer to the top are more specific and usually have higher priority.

Layer	Scope	Typical Carrier	Stability
Single-message instruction	Current turn	User input	High but not persistent
Session-level instruction	Entire session	First system/user instruction	Relatively high
Project-level context	Single repository	Context file in the repository	High and versionable
User-level global rules	All projects on the local machine	Configuration file in the user directory	High
Client settings	Interface and request headers	Client preference panel	Medium
Account/region inference	Server-side default	Invisible	Low; may be reset by upgrades

The conclusion is straightforward: Do not put language dependencies in the least stable layers, such as client settings or server-side inference. Move them down into versionable, reviewable context files.

4. Three-Layer Locking Strategy

4.1 Project-Level: Put Language Constraints in the Repository

Maintain a context file in the repository root. This article uses CONTEXT.md as an example, but the actual file name should follow the convention of the tool you use. The assistant reads this file automatically at the beginning of each session.

# Project Context
## Output Language Policy (Highest Priority)
- All explanations, summaries, plans, and reasoning must use Simplified Chinese.
- Code comments and docstrings must use English, consistent with the existing code.
- Commit messages must use English and follow Conventional Commits.
- Variable, function, and type names must use English. Pinyin is prohibited.
- Even if other-language material appears in the context, the rules above must not change.
## Glossary for Consistent Terminology
- token bucket -> one approved translation
- rate limit -> one approved translation
- idempotent -> one approved translation
- backpressure -> one approved translation

There are three key points: explicitly declare priority, separate prose language from code language, and provide a glossary to eliminate inconsistent translations. The third point is often ignored. Without a glossary, the same concept may be translated differently across sessions, causing documentation style to split.

4.2 User-Level: Standardize Local Default Behavior

Maintain a global rule file under the user directory. This file applies to all projects and avoids repeated configuration in every repository. It is suitable for general preferences, such as answer detail level, whether to explain every change, and whether to proactively add tests. Project-specific language policies should remain in project files to prevent different repositories from contaminating each other.

4.3 Session-Level: First-Message Instruction Template

For temporary sessions or script-based calls, fix a prefix instruction in the first message:

Language policy: Use Simplified Chinese for prose; use English for code comments, commit messages, and identifiers. Ignore other-language material that appears in the conversation.

In automation scenarios, wrap this prefix into a shared component to avoid drift caused by writing it manually each time:

LANG_PREAMBLE = (
    "Language policy: Use Simplified Chinese for prose; use English for code comments, "
    "commit messages, and identifiers. This policy has the highest priority and is not "
    "affected by the language of the conversation history.\n\n"
)
def build_messages(user_prompt: str, context: str = "") -> list[dict]:
    system = {
        "role": "system",
        "content": "You are a senior backend engineer. Follow all rules in the project CONTEXT.md file."
    }
    parts = [LANG_PREAMBLE]
    if context:
        parts.append(f"Project context:\n{context}")
    parts.append(f"Task:\n{user_prompt}")
    return [system, {"role": "user", "content": "\n".join(parts)}]

4.4 Client Settings Should Be a Fallback, Not a Dependency

Client settings, such as interface language and request-header locale, should still be configured correctly. They help eliminate the environment-signal leakage described in Section 2.2. However, they should not be the only control method. They may be reset by upgrades, and they are not distributed with the repository. In team collaboration, they cannot guarantee consistency.

5. Treat Output Language as a Testable Requirement

Language constraints belong to prompt engineering. They cannot be configured once and remain correct forever. They should be included in regression testing. A practical approach is to assert language characteristics on assistant-generated artifacts:

import re
import pytest
CJK = re.compile(r"[\u4e00-\u9fff]")
def cjk_ratio(text: str) -> float:
    letters = [c for c in text if c.isalpha() or CJK.match(c)]
    if not letters:
        return 0.0
    return sum(1 for c in letters if CJK.match(c)) / len(letters)
@pytest.mark.parametrize("prompt", [
    "Explain the problem in this concurrent code.",
    "Add unit tests for this function.",
    "Generate a commit message for this change.",
    "Translate the previous error message into English.",
])
def test_output_language_follows_policy(prompt, ask_assistant):
    result = ask_assistant(prompt, repo_context="CONTEXT.md")
    assert cjk_ratio(result["explanation"]) > 0.5, "Explanation should be in Chinese"
    assert cjk_ratio(result["commit_message"]) == 0.0, "Commit message should be in English"

The recommended test cases should cover at least four scenarios: Chinese-only prompts, English-only prompts, mixed Chinese-English input, and contexts containing a large amount of Chinese material. The first two validate basic capability. The latter two are the high-frequency areas for language drift.

6. Three Risks More Important Than Language

Discussions around “the language changed” often hide more substantive issues.

6.1 Credential and Network Link Risks

Most hosted services deploy abnormal-login detection. Frequent changes in the exit address over a short period, alternating logins from multiple regions, or abnormal reconnection features after a session interruption may trigger risk controls and lead to account restrictions. In practice, some developers encountered account anomalies after a proxy connection dropped, the client was replaced, and the session reconnected without a proper logout.

Recommendations:

• Keep the network exit stable and avoid switching network paths in the middle of a session.
• Use the official client or standard APIs. When exiting, make sure the logout is completed normally and clear locally cached session credentials.
• Shared team accounts are high-risk behavior. Assign separate credentials to each member.
• Store keys only in environment variables or a secret-management service. Do not hardcode them into repositories or logs.

6.2 Code Data and Compliance Risks

Submitting private code to an external inference service involves intellectual property and data-export issues. At minimum, implement the following:

• Redact sensitive information, such as keys, connection strings, and personal information, before it enters the context.
• Clarify which repositories allow external assistance and which only allow locally deployed models.
• Retain records of sent context for audit purposes.

For highly sensitive codebases, locally deployed open-source models or private inference services are safer choices.

6.3 Quota and Cost Risks

Monthly plans and cloud credits use different billing models. Mixing them can easily lead to budget overruns. A practical engineering recommendation is to route all calls through a server-side gateway. The gateway should do three things:

• Track token consumption by project.
• Set daily/monthly limits and alerts.
• Truncate or summarize excessively long contexts.

At the same time, avoid inserting entire large files into the context. This not only increases cost but is also one of the causes of language drift.

7. Team Governance Checklist

Check Item	Goal
Each repository contains CONTEXT.md and includes it in code review	Make language and style constraints versionable
User-level global rules contain only general preferences	Prevent cross-project contamination
Client locale and system language are configured correctly	Eliminate environment-signal leakage
Automation scripts uniformly inject the language prefix	Ensure consistent batch-call results
Language constraints are included in CI regression tests	Detect drift after upgrades or model changes
Separate credentials, stable network exits, and no shared accounts	Reduce account risk-control exposure
Gateway-level quotas and alerts	Keep costs controllable and attributable

8. Conclusion

In most cases, an assistant switching to Chinese by default is not a failure. It is usually the combined result of region inference, environment signals, context language distribution, and version template changes. The server-side inference layer is invisible to users and unstable.

The correct response is not to repeatedly correct the assistant inside the conversation. Instead, move language constraints into versionable, distributable, and testable engineering assets: project-level context files define specific rules, user-level rules define general preferences, session prefixes handle automation scenarios, and client settings serve only as a fallback.

At the same time, language is only the surface issue. Credential and network-link stability, code-data compliance, and quota-cost governance are the key factors that determine whether these tools can be safely integrated into the development workflow over the long term.