ai-translating-content

AI Translating Content

Translate text between languages while preserving brand voice, enforcing glossary terms, and supporting batch i18n workflows — using DSPy signatures and optimizers.

Step 1 - Understand the translation task

Before writing any code, ask:

What content? UI strings, marketing copy, support tickets, help docs, legal text?
What languages? Specific locales (e.g., es-MX vs es-ES) or open-ended?
Do you have a glossary? Brand terms, product names, and technical terms that must not be translated?
Tone/formality? Casual app copy vs formal documentation vs friendly support replies?
Volume? One-off translation vs batch i18n file processing?
Quality bar? Best-effort draft vs publication-ready?

The answers determine whether you need a simple signature or a full pipeline with glossary enforcement and quality scoring.

Step 2 - Build a basic translator

import dspy

lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

class Translate(dspy.Signature):
    """Translate source_text into the target_language. Preserve formatting, tone, and meaning."""
    source_text: str = dspy.InputField(desc="Text to translate")
    target_language: str = dspy.InputField(desc="Target locale, e.g. 'Spanish (Mexico)' or 'fr-FR'")
    translated_text: str = dspy.OutputField(desc="Translation in the target language")

translator = dspy.Predict(Translate)

result = translator(
    source_text="Get started for free",
    target_language="Spanish (Mexico)"
)
print(result.translated_text)
# → "Comienza gratis"

Step 3 - Add glossary enforcement

Brand terms and product names must survive translation unchanged. Pass them explicitly — do not rely on the model to infer them.

from pydantic import BaseModel
from typing import list

class TranslationResult(BaseModel):
    translated_text: str
    glossary_terms_used: list[str]  # terms from the glossary preserved as-is

class TranslateWithGlossary(dspy.Signature):
    """Translate source_text into target_language. Terms listed in glossary must appear
    verbatim in the output — do not translate them."""
    source_text: str = dspy.InputField()
    target_language: str = dspy.InputField()
    glossary: list[str] = dspy.InputField(
        desc="Terms that must NOT be translated (product names, brand terms, technical terms)"
    )
    result: TranslationResult = dspy.OutputField()

translator = dspy.Predict(TranslateWithGlossary)

result = translator(
    source_text="Upgrade to Acme Pro to unlock unlimited Workspaces.",
    target_language="Spanish (Mexico)",
    glossary=["Acme Pro", "Workspaces"]
)
print(result.result.translated_text)
# → "Actualiza a Acme Pro para desbloquear Workspaces ilimitados."
print(result.result.glossary_terms_used)
# → ["Acme Pro", "Workspaces"]

Step 4 - Locale-aware tone

Pass tone as an explicit input field. Do not rely on the model to infer formality from the locale.

class TranslateLocaleAware(dspy.Signature):
    """Translate source_text into target_language using the specified tone.
    Glossary terms must appear verbatim."""
    source_text: str = dspy.InputField()
    target_language: str = dspy.InputField(desc="Target locale, e.g. 'pt-BR' or 'German (formal)'")
    tone: str = dspy.InputField(
        desc="One of: casual, neutral, formal. Controls register and vocabulary."
    )
    glossary: list[str] = dspy.InputField(default=[])
    translated_text: str = dspy.OutputField()

translator = dspy.Predict(TranslateLocaleAware)

result = translator(
    source_text="Hey! Check out what's new this week.",
    target_language="French (France)",
    tone="casual",
    glossary=[]
)

Step 5 - Batch translation for i18n files

Translate each key individually. Do not concatenate all strings into one call — that degrades quality and makes glossary enforcement unreliable.

import json

def translate_i18n_file(
    source_path: str,
    target_language: str,
    glossary: list[str],
    tone: str = "neutral"
) -> dict:
    with open(source_path) as f:
        strings = json.load(f)  # {"key": "English string", ...}

    translator = dspy.Predict(TranslateLocaleAware)
    translated = {}

    for key, text in strings.items():
        result = translator(
            source_text=text,
            target_language=target_language,
            tone=tone,
            glossary=glossary
        )
        translated[key] = result.translated_text

    return translated

# Usage
es_strings = translate_i18n_file(
    source_path="locales/en.json",
    target_language="Spanish (Mexico)",
    glossary=["Pro", "Workspace", "Dashboard"],
    tone="casual"
)

with open("locales/es-MX.json", "w") as f:
    json.dump(es_strings, f, ensure_ascii=False, indent=2)

For large files, add a progress bar and rate-limit retries:

from tqdm import tqdm

for key, text in tqdm(strings.items(), desc="Translating"):
    ...

Step 6 - Quality estimation per segment

Add a confidence score output to flag segments that need human review.

class TranslationWithQuality(BaseModel):
    translated_text: str
    confidence: float  # 0.0–1.0
    needs_review: bool
    review_reason: str  # empty string if needs_review is False

class TranslateWithQuality(dspy.Signature):
    """Translate source_text into target_language. Also estimate translation confidence:
    1.0 = straightforward, <0.7 = ambiguous or idiom-heavy, flag for human review."""
    source_text: str = dspy.InputField()
    target_language: str = dspy.InputField()
    glossary: list[str] = dspy.InputField(default=[])
    result: TranslationWithQuality = dspy.OutputField()

translator = dspy.Predict(TranslateWithQuality)

result = translator(
    source_text="We'll circle back on this once we've boiled the ocean.",
    target_language="Japanese",
    glossary=[]
)

if result.result.needs_review:
    print(f"Review needed: {result.result.review_reason}")

Step 7 - Test and optimize

Glossary compliance metric

def glossary_compliance(example, pred, trace=None):
    glossary = example.glossary
    translated = pred.result.translated_text if hasattr(pred, "result") else pred.translated_text
    # All glossary terms must appear verbatim in the translation
    return all(term in translated for term in glossary)

Meaning preservation judge

class MeaningPreservationJudge(dspy.Signature):
    """Given a source text and its translation, judge whether the meaning is fully preserved.
    Return a score from 0 to 1."""
    source_text: str = dspy.InputField()
    translated_text: str = dspy.InputField()
    target_language: str = dspy.InputField()
    score: float = dspy.OutputField(desc="0.0 = meaning lost, 1.0 = meaning fully preserved")

judge = dspy.Predict(MeaningPreservationJudge)

def meaning_preserved(example, pred, trace=None):
    translated = pred.result.translated_text if hasattr(pred, "result") else pred.translated_text
    result = judge(
        source_text=example.source_text,
        translated_text=translated,
        target_language=example.target_language
    )
    return result.score >= 0.8

Combined metric and optimization

def translation_metric(example, pred, trace=None):
    return (
        glossary_compliance(example, pred) and
        meaning_preserved(example, pred)
    )

trainset = [
    dspy.Example(
        source_text="Upgrade your plan today.",
        target_language="German",
        glossary=["Pro", "Dashboard"]
    ).with_inputs("source_text", "target_language", "glossary"),
    # add more examples...
]

optimizer = dspy.MIPROv2(metric=translation_metric)
optimized_translator = optimizer.compile(
    dspy.Predict(TranslateWithGlossary),
    trainset=trainset
)

When NOT to use AI translation

Situation	Better approach
High-volume, low-stakes strings (e.g., product descriptions at scale)	DeepL API or Google Cloud Translation
Legal, medical, or regulated documents	Certified human translators
Single-word lookups or dictionary queries	Static lookup table or dictionary API
Real-time chat translation at high throughput	Streaming DeepL/Google with caching

DSPy shines when you need glossary enforcement, tone control, quality estimation, or want to optimize translation prompts against a metric.

Key patterns

Pattern	When to use
`dspy.Predict`	Single-string translation
`dspy.Predict` + Pydantic output	Glossary enforcement, quality scoring
Batch loop per key	i18n JSON/YAML file translation
`dspy.MIPROv2`	Optimize for glossary compliance or fluency
`dspy.Refine`	Retry failed glossary terms or low-confidence segments

Gotchas

Claude translates glossary terms instead of keeping them as-is. Name the field glossary and state in the docstring "do not translate these terms — they must appear verbatim." Listing them inline in the docstring also helps. If terms still get translated, switch to dspy.ChainOfThought so the model reasons about each term explicitly.

Claude outputs the source language when target languages are closely related. For pairs like en → pt-PT or es-ES → es-MX, Claude sometimes returns the source text unchanged or barely modified. Always use the full locale label ("Portuguese (Portugal)", "Spanish (Mexico)") rather than a BCP-47 tag alone.

Claude uses dspy.Assert/dspy.Suggest for glossary enforcement — use dspy.Refine instead. Assert/Suggest are deprecated in DSPy 3.x. Use dspy.Refine with a reward function that checks glossary term presence. See /dspy-refine for the pattern.

Claude generates overly formal translations for casual UI copy. Pass tone as an explicit input field with a value like "casual". Do not rely on the model to infer register from the locale. French and German LM outputs default to formal register unless explicitly instructed otherwise.

Claude batch-translates by concatenating all strings into a single LM call. This causes glossary drift, misattributed translations, and subtle meaning errors across long batches. Translate each string individually or in small batches of 3-5 closely related strings (e.g., a dialog's button labels together).

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

/dspy-refine - retry low-confidence segments or failed glossary enforcement with a reward function
/dspy-best-of-n - sample N translations and select the best by glossary compliance + fluency score
/ai-improving-accuracy - optimize translation prompts with MIPROv2 against a labeled dataset
/ai-checking-outputs - validate translated output structure, glossary compliance, and format
/ai-generating-data - generate synthetic parallel sentences to build a translation training set
Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do

Additional resources

See examples.md for worked examples:

Marketing copy translator (English to Spanish with brand glossary)
i18n JSON batch translator
Support ticket translator with confidence scoring