Adding AI translation to a product, pipeline, or process sounds simple until it isn’t. The wrong integration method, a missed rate limit, an unhandled async response, a cost model you didn’t account for — any of these can turn a straightforward project into a debugging session.
This guide collects every question worth asking before you commit to an approach. It covers what AI translation can and can’t do, how to choose the right integration method, how billing and rate limits work, and what to verify before you go live. Most questions are tool-agnostic. Where a specific implementation detail matters, we note how Lara Translate handles it.
TL;DR
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What AI Translation Can (and Can’t) Do
1. How does AI translation differ from traditional machine translation?
Traditional machine translation (MT) systems, like phrase-based or early neural models, translate sentence by sentence, largely without awareness of the surrounding context. Modern AI translation engines combine large language models (LLMs) with neural machine translation (NMT) to reason across the full document or conversation, handle ambiguity, and adapt tone and terminology. The result is more fluent, more context-aware output — but it also means the quality depends heavily on how much context you give the engine.
2. How many languages does a typical AI translation API support?
It varies significantly by provider. Some support fewer than 50 languages well; others cover over 200. Full Right-to-Left (RTL) support — Arabic, Hebrew, Persian — is not universal, so explicitly check if you need it. Lara Translate supports 207 languages with full RTL rendering. See the full language list.
3. What kinds of content does AI translation handle well?
AI translation performs best on well-structured, unambiguous text: business documents, support content, product descriptions, UI strings, technical documentation, and subtitles. It handles poorly written or heavily idiomatic source text less reliably — garbage in, garbage out still applies. The more context you provide (domain, audience, tone), the better the output. How to evaluate translation quality.
4. What kinds of content does AI translation handle poorly?
Highly creative content (poetry, wordplay, cultural humor), legal text requiring certified accuracy, highly technical jargon without reference material, and ambiguous source text are all challenging. For these, AI translation should be treated as a first draft for human review, not a final output. How to request a human review on top of AI translations.
5. Can AI translation preserve the original document’s formatting?
Yes, if the API or tool you’re using is designed for document translation. A good document translation API preserves tables, headings, inline formatting, captions, and embedded elements. Pure text translation APIs don’t — they return translated strings only and it’s up to you to reconstruct the layout.
6. What is translation quality assessment (LQA), and should I use it?
Linguistic Quality Assessment (LQA) is an automated scoring layer that evaluates the quality of a translated output. Some AI translation engines include it natively, flagging segments as excellent, acceptable, or ambiguous. It’s most useful in production pipelines where you need a confidence signal before delivering output to end users — for example, to decide whether to route a segment to human review. Lara Translate’s LQA automatically tags each translation as Excellent, Good, or Ambiguous Source. Read more about LQA in Lara.
7. Can AI translation enforce specific terminology?
Yes, through glossaries. A glossary is a controlled vocabulary — a list of terms and their approved translations — that the engine applies exactly during translation. This is essential for product names, legal terms, brand-specific language, or any term that must never vary. Most enterprise-grade APIs support glossary attachment at request time. How glossaries work in Lara.
8. What is a Translation Memory, and does the API use it?
A Translation Memory (TM) stores previously translated segments. When the same or similar sentence appears again, the engine reuses the approved translation instead of generating a new one. This enforces consistency across long documents or repeat jobs and can reduce costs. Not all AI translation APIs support TMs natively — verify before committing to an architecture that depends on it. What is a Translation Memory?
9. Can AI translation detect the source language automatically?
Most modern AI translation APIs include language detection. You pass the text without specifying a source language, and the engine returns its best prediction along with a confidence score. Some allow you to pass a hint or a passlist of candidate languages to narrow the prediction. It’s useful for user-generated content where the input language is unknown, but explicit source language specification is always more reliable when you know it. How language detection works in Lara.
10. Is there a difference between translating plain text and translating HTML?
Yes, and it matters. When you pass HTML to a translation API, the engine needs to know it’s HTML — otherwise it may translate tag attributes, break markup, or mangle inline elements. A proper implementation uses a content_type: text/html flag (or equivalent), which tells the engine to translate only the visible text nodes and leave tags, attributes, and URLs intact. How Lara handles HTML content types.
11. Can AI translation handle multilingual source content?
Some engines can detect and handle documents where different segments are written in different languages, translating only the segments that aren’t already in the target language. This is useful for mixed-language support tickets, forums, or user-generated content. Verify that the API you’re using supports per-segment language detection rather than document-level only.
Choosing the Right Integration Method
12. What integration options are typically available for AI translation?
Most AI translation providers offer some combination of: a REST API, language-specific SDKs, a CLI tool, no-code connectors (n8n, Zapier, Make), browser extensions, CAT tool plugins, and increasingly, MCP servers for AI agent workflows. The right choice depends on your technical stack, the volume of translation, and whether you need real-time or batch processing. Accessing Lara’s API.
13. When should I use the REST API directly versus an SDK?
Use the REST API directly when you’re working in a language without an official SDK, when you want minimal dependencies, or when you’re calling from a serverless function or shell script. Use an SDK when one exists for your language — SDKs handle authentication signing, request formatting, retries, and response parsing for you, which reduces boilerplate and error surface. Lara provides SDKs for Python, Node.js, Java, PHP, Go, .NET, and Swift. Lara SDK quickstart and documentation.
14. What is an MCP server and when does it make sense for translation?
An MCP (Model Context Protocol) server is a standardized interface that lets AI agents call external tools — including translation — directly within an LLM workflow. If you’re building AI-powered products where an LLM needs to translate content as part of a larger task (summarization, multilingual chat, content generation), connecting to a translation MCP server offloads that work to a specialized translation model rather than asking the general LLM to translate. This reduces hallucination risk, enforces terminology, and separates translation quality from LLM reasoning quality. Lara offers an MCP server compatible with Claude Desktop and A2A Protocol multi-agent workflows. Getting started with the Lara MCP server · Why use a dedicated translation engine inside an LLM · Install Lara on Claude Desktop.
15. When should I use a CLI tool instead of the API?
A CLI is the right choice for localization workflows that live in code repositories — translating JSON resource files, PO files, Markdown content, or Vue components as part of a build or release process. CLI tools integrate natively with CI/CD pipelines (GitHub Actions, GitLab CI) and can be triggered on file changes without writing any application code. Lara’s CLI (lara-cli translate) supports JSON, PO, TS, Vue, Markdown, and Android XML with a hierarchical lara.yaml configuration file. Getting started with the Lara CLI.
16. When does a no-code connector (n8n, Zapier, Make) make sense?
When the people managing the workflow aren’t developers, or when you want to connect translation to other tools (CRMs, email platforms, file storage, forms) without custom code. No-code connectors are ideal for automating document translation on form submission, translating incoming support tickets, or syncing translated content to a CMS. The trade-off is less control over request parameters and rate limit handling compared to the API. Lara API and integrations overview.
17. I work in Google Sheets. Can I call translation directly from a cell formula?
Yes, if the translation provider has a Google Sheets integration. Lara Translate provides =LARATRANSLATE() for cell-level translation and =LARAEVAL() for automatic quality scoring, allowing spreadsheet-based localization workflows without any API setup. How to install the Lara extension for Google Sheets.
18. What is the difference between real-time and batch translation via API?
Real-time (synchronous) translation returns the result immediately — useful for user-facing features where latency matters. Batch translation processes large volumes asynchronously — you submit a job, poll for completion, and retrieve results. Batch is typically cheaper per character and better suited for processing large document sets or overnight jobs. Some providers offer explicit batch endpoints with different pricing; others just have different latency SLAs. Lara offers a dedicated Lara Batch model that can be up to 10x cheaper than standard pricing for high-volume jobs.
19. Do I need to be a developer to use an AI translation API?
Not always. Many providers offer no-code paths — browser extensions, Google Sheets add-ons, CMS plugins, n8n nodes — that give non-developers access to the same underlying API without writing code. The API itself is worth understanding conceptually even if you don’t call it directly, because it determines what’s possible downstream. Lara browser extension for non-developers.
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Authentication & Setup
20. How does authentication work for translation APIs?
Most translation APIs use API key authentication — you generate a key from the provider’s dashboard and include it in every request header. Some providers use a single API key; others use a key pair (an Access Key ID and an Access Key Secret), where the secret is used to sign requests. Store credentials in environment variables, never in source code. How to generate a Lara API key.
21. What is the Access Key ID / Access Key Secret pattern?
This is a credential pair where the ID is a public identifier, and the Secret is used to sign or authenticate requests. The secret is typically shown only once at creation — if you lose it, you need to rotate the key. Lara uses this pattern: the SDK handles signing automatically, so you only need to set the two environment variables. What to do if you lose your API key.
22. How should I store API credentials securely?
Use environment variables in development and a secrets manager (AWS Secrets Manager, HashiCorp Vault, GitHub Actions secrets, Doppler) in production. Never hardcode credentials in source files or commit them to a repository. Rotate keys if they’re ever exposed.
23. Can I use the same API key across multiple environments (dev, staging, prod)?
You can, but it’s a better practice to generate separate keys per environment. This lets you rotate or revoke a compromised key without affecting all environments, and gives you cleaner usage reporting per environment.
24. Is there a free tier to test with before committing?
Most major providers offer a free tier or trial. Lara’s Free API tier includes 10,000 characters per month with a rate limit of 1,000 characters per second — enough to build and test a basic integration. Free-tier web outputs include a “Translated with laratranslate.com” attribution signature. Lara API Free plan details.
25. How do I make my first translation API call?
The minimum viable call requires: your credentials, a source text, a target language, and an endpoint URL. In most SDKs, this is three to five lines of code. For Lara using the Python SDK:
from lara import Translator
translator = Translator(access_key_id="...", access_key_secret="...")
result = translator.text.translate(text=["Hello world"], target="it-IT")
print(result[0].translation)Full quickstart guide in the Lara developer hub.
Translating Text via API
26. What is the basic structure of a text translation request?
At minimum: the text to translate (a string or array of strings), the target language, and optionally the source language. Additional parameters — context, style, glossary IDs, TM IDs, privacy flags — layer on top. Keeping the base call simple and adding parameters incrementally is the best way to isolate quality issues during development. Lara text translation API reference.
27. Can I translate multiple strings in a single API call?
Yes. Most APIs accept an array of strings in a single request, which is more efficient than looping individual calls. There’s typically a maximum array size — Lara supports up to 128 text blocks per request. Batching reduces round-trip overhead and is especially important when translating UI strings or short content at scale.
28. How do I pass context to improve translation quality?
Most APIs have a context or instructions parameter where you can describe the content domain, intended audience, tone, or any disambiguation notes. “This is a legal contract for B2B SaaS customers” or “Translate informally, target audience is teenagers” both meaningfully change the output. Keep instructions short and specific — vague instructions are ignored or misapplied. What context to provide to Lara · common use cases with examples · adapt to context (API) · adapt to instructions (API).
29. How do I prevent certain strings from being translated?
Pass them as non-translatable blocks. In Lara’s API, the text field accepts an array of objects with a translatable: false flag — these blocks are passed as surrounding context for the AI without being translated themselves. This is useful for product names, brand terms, or placeholder strings that must appear verbatim in the output.
30. How do I handle HTML content in translation requests?
Set content_type: text/html in the request. The engine will parse the HTML, extract translatable text nodes, translate them, and reconstruct the markup. Inline tags supported by Lara include <g>, <ph>, and <mrk>. Never send raw HTML to a plain text endpoint — the tags will be treated as translatable text and broken. Content type handling in the Lara API.
31. What translation styles or modes are typically available?
Style controls how the engine balances literal accuracy against natural fluency. Common options: Faithful (close to source, structure-preserving — good for legal, technical, medical), Fluid (natural and readable — good for business content), Creative (expressive, idiomatic — good for marketing and transcreation). Not all providers expose this as an explicit parameter — some fold it into model selection. Translation styles in Lara.
32. What is a reasoning or “think” model and when should I use it?
A reasoning-oriented translation model performs multi-step linguistic analysis before producing output — checking for ambiguity, verifying terminology, and self-correcting. It’s slower (typically 5–10x) and more expensive, but meaningfully more accurate for complex or high-stakes content. Lara’s Lara Think model detects and corrects around 80% of major linguistic issues. Use it for legal, medical, or regulatory content where a mistranslation has real consequences. Activate it with reasoning: true in the API request.
33. Is there an ultra-low latency option for real-time applications?
Some providers offer a fast-path model optimized for latency over maximum quality. Lara offers Lara Flash, which achieves ~50ms median latency — suitable for real-time chat translation, live UI updates, or any user-facing feature where response time matters more than maximum accuracy.
34. How do I handle profanity or sensitive language in user-generated content?
Some APIs include a profanity filter parameter with options to detect, hide, or avoid profanity in the output. This matters for platforms with mixed or unknown user input. Lara’s API supports profanityFilter: detect | hide | avoid. Full parameter reference in the Lara developer hub.
35. What is translation caching and should I enable it?
Translation caching stores the result of a translation request so that the same input returns the same output without re-running the model. It reduces cost and latency for high-repetition content (UI strings, standard disclaimers, templated emails). The trade-off is that cached results don’t reflect glossary or TM updates until the cache expires. Lara offers caching with a configurable TTL (cache_ttl), available on request.
Translating Documents via API
36. How does document translation via API differ from text translation?
Document translation is asynchronous: you upload the file, receive a job ID, poll until the job completes, then download the translated file. Text translation is synchronous — you send a request and get a response immediately. The async model is necessary because large documents can take seconds to minutes to process. Lara document translation API reference · API documentation overview.
37. What file formats does AI translation typically support via API?
Enterprise-grade APIs support a wide range: Office documents (DOCX, XLSX, PPTX), PDF, InDesign (IDML), FrameMaker (MIF), XLIFF, subtitles (SRT, VTT), data formats (JSON, CSV, XML, YAML), web formats (HTML, Markdown), and localization files (PO, RESX, STRINGS). Lara supports exactly 70 file formats. Always verify format support before designing a workflow around a specific file type. Supported file formats in Lara.
38. What is the minimum billing threshold for document translation?
Many providers apply a minimum character count per document, regardless of actual content size. Lara applies a 20,000-character minimum per file on paid plans — a 5,000-character document is billed as 20,000 characters. Factor this into your cost model when processing many small files.
39. How do I attach Translation Memories and Glossaries to a document API request?
Pass the TM and glossary IDs as parameters in the upload request. The engine applies them during translation, enforcing approved terminology and reusing previously translated segments. This is particularly important for versioned documents where you want the delta (new or changed segments only) to carry forward your approved translations. Document API parameters in the Lara developer hub.
40. Can I get the translated document back as a PDF instead of an editable format?
If the provider supports it. Lara allows you to pass outputFormat: "pdf" in the API request when translating a PDF — the default returns an editable DOCX. This matters when the consumer of the output needs a non-editable file, or when you want to preserve the original layout exactly as rendered.
41. How do I handle DOCX files with tracked changes via API?
Pass acceptRevisions: true inside extractionParams to automatically accept all tracked changes before translation begins. Without this, the translation may include revision markup, producing unreliable output. The equivalent operation in the web UI is a manual prompt before upload.
42. How do I include comments from a Word document in the translation job?
Pass extractComments: true inside extractionParams. Comments are then included as translatable content alongside the main body text.
43. What happens to formulas in an Excel file when I translate it?
A properly implemented document translation API auto-detects and skips cell formulas — only visible text content is translated. If formulas are being translated, the file may have cells formatted as plain text rather than formula cells. Fix the formatting before uploading. How to translate an Excel file with Lara.
44. How do I handle image translation inside a PDF via API?
OCR-based image translation inside PDFs must be explicitly requested — it isn’t on by default. In Lara’s API, enable the “Translate images” flag at upload time. Characters extracted via OCR count toward your usage quota separately from standard document characters. OCR in document translation with Lara.
Quality, Glossaries & Translation Memories
45. How do I create and manage a glossary via API?
Glossary management endpoints typically allow you to create, update, list, export, and delete glossaries programmatically. You can add individual terms or import a CSV file in bulk. Lara’s API supports both monodirectional glossaries (source → target, CSV with csv/table-uni content type) and multidirectional glossaries (concept-based with a shared guid across all language pairs, csv/table-multi). Glossary management API reference · CSV formatting guidelines for Lara glossaries.
46. What is the difference between a monodirectional and a multidirectional glossary?
A monodirectional glossary maps terms in one source language to one target language. A multidirectional glossary uses a concept-based structure with a unique identifier per term, linking translations across all language pairs simultaneously — useful when you translate the same content into many languages and want a single source of truth for terminology. How glossaries work in Lara · Glossary management API reference.
47. How do I create and manage Translation Memories via API?
TM management endpoints let you create memories, add or delete individual translation units, import TMX files in bulk, and check import status. You can also create TMs from external sources like MyMemory. Lara supports granular TU control, including sentence context fields (sentence_before, sentence_after) for disambiguation. Translation Memory API reference.
48. Can I import an existing TMX file programmatically?
Yes. Most enterprise APIs support async TMX import — you upload the file, receive a job ID, and poll for completion. This is the right approach for migrating an existing translation memory from a CAT tool into an API-based workflow.
49. How do I use the API to evaluate the quality of an existing translation?
Some providers offer a dedicated quality evaluation endpoint that scores a source-translation pair. Lara’s Google Sheets add-on uses =LARAEVAL() for this; at the API level, LQA scores are returned as part of the translation response. This is useful for scoring translations produced outside of Lara before adding them to a TM. LARAEVAL in Google Sheets.
Privacy, Security & Data Handling
50. Does the translation provider store my content after translation?
It depends on the provider and the mode. Most store translations by default to build translation memories and improve models. Check the privacy policy carefully — specifically whether you can opt out of storage, whether data is used for model training, and what the retention period is. Lara’s Learning vs. Incognito modes explained.
51. What is zero-retention or Incognito Mode in a translation API?
Zero-retention mode means the provider processes the translation and immediately deletes it — nothing is stored, logged, or used for training. In Lara’s API, pass "noTrace": true (REST) or no_trace: true (SDK) to activate this. Use it for any content covered by NDA, attorney-client privilege, or regulatory data handling requirements. How Incognito Mode works.
52. Is a translation API GDPR-compliant?
It depends on the provider. Key questions: Where is data processed? Who are the subprocessors? Is a Data Processing Addendum (DPA) available? Does the provider comply with Standard Contractual Clauses (SCCs) and the EU-US Data Privacy Framework? Lara is an Italian company, fully GDPR-compliant, with a DPA available for enterprise agreements. Lara and GDPR compliance.
53. Can I request a Data Processing Addendum (DPA)?
Enterprise-grade providers typically offer DPAs for paid plans. A DPA governs how the provider handles your data as a data processor, assigns responsibilities, and specifies security obligations. It’s a prerequisite for most enterprise procurement processes. Privacy and data protection at Lara.
54. Who are the typical subprocessors for an AI translation API?
Common subprocessors include cloud infrastructure providers (AWS, GCP, Azure), error monitoring services (Sentry), payment processors (Stripe), and analytics tools. Lara’s documented subprocessors include AWS, Sentry, Stripe, Google Analytics/Ads/Workspace, HubSpot, ActiveCampaign, PostHog, Cookiebot, Meta, and LinkedIn. Lara’s subprocessor list.
55. How do I handle translation of content that includes personal data (PII)?
Use zero-retention mode for any content containing names, addresses, financial data, or health information. Confirm the provider’s DPA covers PII processing. For regulated industries (healthcare, finance, legal), get legal sign-off on the data flow before building. Is Lara trusted? Security overview.
Translate sensitive content with zero data retention
One parameter. Pass noTrace: true in your API request and Lara processes your content without storing, logging, or training on it.
Rate Limits, Billing & Cost Modelling
56. How are translation API costs typically calculated?
Most providers bill by character count — the number of characters in the source text. Some bill by word or by “page” for document translation. Pricing tiers typically differ by volume (per million characters), plan (free, pro, team), and model (standard vs. reasoning/quality model). Always check whether the minimum billing unit per request could affect your cost model for short strings. Lara API pricing breakdown.
57. What is a rate limit and how does it affect my integration?
A rate limit is the maximum throughput the API allows per unit of time — typically characters per second. Exceeding it returns a 429 error. Design your integration to respect rate limits by throttling requests, queuing large jobs, and implementing exponential backoff on 429 responses. Lara’s rate limits are 1,000 characters/second on the Free plan and 10,000 characters/second on paid plans. Lara API rate limits and plans.
58. How do I estimate the cost of translating a given content volume?
Count the total characters in your source content (most languages average 5–6 characters per word). Multiply by the per-character rate for your target plan and model. Add a buffer for document minimums if you’re translating files. For languages with short words (English, Dutch) the count will be lower than for agglutinative languages (Finnish, Turkish, German). Image translation and audio translation have separate pricing — flat per image and per minute respectively.
59. Is there a minimum charge per API request or per document?
Yes, for document translation on most paid plans. Lara applies a 20,000-character minimum per document — a 3,000-character file is still billed as 20,000 characters. For text translation API calls there is no minimum per request, but Free plan API usage has a monthly cap. Free plan limits and paid plan comparison.
60. How does the reasoning / quality model affect pricing?
Reasoning models are priced at a significant premium over standard models — often 50–100x higher per character. Lara Think is priced at €2,000/$2,499 per million characters on Pro (vs. €20/$24.99 for standard). Use it selectively: identify the content types in your pipeline that genuinely require it, and route everything else to the standard model. Lara model pricing comparison.
61. How do I monitor API usage programmatically?
Most providers offer a usage dashboard and/or a usage API endpoint. Build usage tracking into your integration from day one — especially on Team plans where a shared character pool can be exhausted by a single heavy job. Set up alerts before you hit quota limits, not after. How to track API usage on Lara.
62. What happens when I exceed my quota?
Behavior varies: some providers return an error immediately; others queue requests and process them when quota resets. Know your provider’s behavior before it affects production. On Lara’s Team plan, character quotas for text are pooled; document page limits are per user. Interpreter minutes are not shared.
Going to Production
63. What error codes should my integration handle?
At minimum: 400 (bad request — malformed parameters), 401 (authentication failure — check credentials), 403 (authorization failure — check permissions), 429 (rate limit exceeded — backoff and retry), 500/503 (server error — retry with exponential backoff). For async document translation, also handle job status errors in the polling loop. Lara API reference and error codes.
64. How should I implement retry logic for translation API calls?
Use exponential backoff with jitter for retryable errors (429, 503). Don’t retry on 400 or 401 — these indicate a problem with your request or credentials that won’t resolve on its own. Set a maximum retry count and surface failures to your logging/alerting system rather than silently swallowing them.
65. How do I handle timeouts for long-running document translation jobs?
Document translation is asynchronous by design — don’t set a tight timeout on the upload call. Instead, poll the status endpoint with a reasonable interval (e.g., every 5–10 seconds) and set a maximum wait time appropriate to your SLA. If a job exceeds your timeout, surface it as a failure and investigate via the provider’s dashboard. Lara’s API allows a custom timeout_in_millis up to 300,000ms (5 minutes) for individual calls.
66. Should I validate translation output before delivering it to end users?
Yes, especially in the early stages of a new integration. Check for obviously broken output: untranslated segments, garbled characters, truncated text, or HTML that didn’t render correctly. Use LQA scores if available. As you gain confidence in the pipeline, you can relax validation — but always keep a mechanism to flag anomalies.
67. How do I test my integration without consuming paid quota?
Use a free tier account or sandbox environment for development. Keep a set of representative test strings that cover your actual content types — short UI strings, long paragraphs, HTML content, text with technical terminology. Test edge cases: empty strings, very long inputs, content with special characters, RTL text. Lara Free API for development and testing.
68. What should I log from translation API calls?
At minimum: request timestamp, character count, source and target language, model used, response time, status code, and any error messages. For document jobs: job ID, upload timestamp, poll attempts, and final job status. This gives you enough data to debug quality issues, track costs, and identify performance bottlenecks.
69. How do I handle fallback if the translation API is unavailable?
Decide upfront what your fallback behavior is: show the original untranslated text, queue the request for later processing, or surface an error to the user. The right answer depends on your use case — a live chat feature needs a different fallback than a background localization pipeline. Build the fallback path before you need it.
70. Is there a callback or webhook option for async document translation?
Some providers support callbacks — a webhook URL you provide that gets called when a job completes, rather than you having to poll. This is preferable for production pipelines because it eliminates unnecessary polling requests. Check whether your provider supports it; if not, polling with exponential backoff on the status endpoint is the standard approach. Async document workflow in the Lara developer hub.
Localization Workflows & CI/CD
71. How does AI translation fit into a software localization workflow?
AI translation sits between string extraction and human review. The typical flow: extract translatable strings from source code → send to translation API → review output (human or LQA-gated) → commit translations back to the repo. The API handles the volume; humans handle edge cases and quality gates. Translation Memories ensure consistency across releases.
72. What file formats are commonly used in software localization pipelines?
JSON (React, Vue, Next.js), PO/POT (gettext, Django, WordPress), RESX (.NET), STRINGS (iOS), Android XML, XLIFF (cross-platform standard), and TS (Qt). A CLI-based translation tool should natively support these formats with parser configurations that know which keys to translate and which to skip. Lara CLI supported formats and parsers.
73. How do I integrate translation into a GitHub Actions CI/CD pipeline?
Use the translation provider’s CLI or API in a workflow step triggered on push or pull request to the relevant locale files. The step extracts new/changed strings, sends them for translation, and commits the results back. Lara’s CLI integrates natively with GitHub Actions and uses a lara.yaml config file for project-level, file-level, and key-level settings. Lara CLI and GitHub Actions integration guide.
74. How do I avoid retranslating strings that haven’t changed?
Attach a Translation Memory to every API request. Unchanged strings that are already in the TM are returned immediately without re-running the model. Only new or modified strings incur translation cost. This is the standard approach for maintaining large localization sets across frequent releases.
75. How do I handle placeholder variables in translatable strings?
Variables like {username}, %s, or {{ count }} must survive translation intact. Most professional translation APIs can be instructed to treat these as untranslatable tokens. In Lara, you can pass them as non-translatable blocks or use the instructions parameter to specify that placeholders should be preserved. Always test with strings containing variables — they’re a common source of broken output.
76. What is a hierarchical configuration file for a translation CLI?
A configuration file (like Lara’s lara.yaml) that lets you define translation settings at multiple levels: project-wide defaults, file-level overrides, and key-level exceptions. For example, you might set style: faithful at the project level, override it to style: fluid for marketing copy files, and mark specific keys as non-translatable. This reduces per-call parameter management and makes localization pipelines reproducible. lara.yaml configuration reference.
Troubleshooting & Common Mistakes
77. My translated output contains broken HTML. What went wrong?
You almost certainly sent HTML to a plain text endpoint without setting content_type: text/html. The engine treated the tags as translatable text, translated attribute values, and broke the markup. Set the correct content type and re-run. Content type handling in the Lara API.
78. The API is returning untranslated text. What should I check?
First: Did you specify the correct target language code? Language codes must match the provider’s accepted format exactly (e.g., it-IT not it or Italian). Second: Is the text in the source language you specified? If source and target are the same, most engines return the original unchanged. Third: Is the content in a non-translatable block?
79. My glossary terms aren’t being applied. Why?
Check that: (1) you’re passing the correct glossary ID in the request, (2) the glossary contains the correct source language and the exact form of the term as it appears in your text (glossaries are case-sensitive), (3) the glossary is active and not empty. Also verify that the term actually appears in the source text — glossaries only apply to terms present in the input. How glossaries work in Lara.
80. I’m getting a 429 error. What should I do?
You’ve exceeded your rate limit. Implement exponential backoff: wait, retry, wait longer, retry again. Add a queue or throttle layer in front of your translation calls if you’re processing high volumes. If you consistently hit rate limits at your usage level, contact the provider about upgrading your plan or requesting a higher limit. Lara API rate limits.
81. My async document job never returns a “completed” status. What do I do?
Check the status endpoint directly — the job may have failed with an error status rather than hanging. If it’s genuinely stuck, check whether the file was valid (not corrupted or password-protected without providing the password), whether the file format is supported, and whether the upload itself completed successfully. Contact support with the job ID if the issue persists. Lara’s document translation API and job status.
82. The translation quality is inconsistent across similar content. What’s causing it?
Likely causes: no context or instructions being passed, no glossary attached (so terminology varies), no TM attached (so the same sentence gets translated each time differently), or mixed source quality. Start by attaching a TM — this alone often resolves consistency issues across a content series. How to evaluate and improve translation quality · How TMs improve consistency in the API.
83. A specific term keeps being translated incorrectly despite my glossary. What should I check?
Verify that the term in the glossary exactly matches the form in the source text — whitespace, punctuation, capitalization. Glossaries are applied case-sensitively and require exact string matching. If the term appears in different forms (plural, declined, abbreviated), you may need separate glossary entries for each form. Glossary CSV formatting guidelines.
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