How do I choose between Modern Standard Arabic and a dialect for my content?
Choose MSA for formal, pan-Arab communications such as legal, technical, and press releases. Use dialects (Egyptian, Levantine, Gulf) for consumer-facing marketing, social copy, or dialogs where local idioms improve engagement. If unsure, generate both: produce an MSA draft and a dialect variant, then test with small audience segments.
What prompt patterns preserve brand names, acronyms, and product codes?
Use a named-entity preservation pattern that explicitly lists items to keep unchanged. Example: "Translate text but keep all uppercase acronyms and proper nouns unchanged (e.g., ACME, SKU-123): {source_text}." Attach a named-entity list to the prompt to avoid accidental translation of brand assets.
How can I ensure right-to-left text and HTML markup survive translation?
Use markup-safe prompts that instruct the model to translate visible text only and preserve tags/placeholders. Include examples of placeholder syntax used in your CMS (e.g., {{user_name}}, <strong>…</strong>) and request output wrapped in the same tags so the output is paste-ready for RTL environments.
Which prompts work best for subtitles and short UI copy?
Use subtitle and brevity patterns that enforce a per-line character limit and natural segmentation (e.g., max 42 chars per subtitle line). For UI/SMS, include explicit length constraints and ask for concise alternatives or microcopy variants within the prompt.
How do I include a glossary or mandatory term list in a prompt?
Embed the glossary as a plain list or link and reference it in the prompt with a clear instruction such as: "Use the following glossary and replace entries accordingly; do not translate product names: {glossary}." For large glossaries, provide the most critical terms inline and attach the full CSV as a context artifact in the automation step.
What is a practical back-translation QA prompt chain for reviewers?
A simple chain: 1) Translate source to Arabic with glossary enforcement. 2) Back-translate resulting Arabic to the source language. 3) Summarize differences and flag segments with meaning drift. Example prompt for step 3: "Back-translate to English and list discrepancies between original and back-translation in bullets, highlighting altered meaning or missing content."
How to adapt prompts for legal and technical translations to reduce ambiguity?
Use conservative phrasing and preserve clause/section numbers. Ask the model to flag ambiguous terms for human review rather than guessing. Example: "Translate legal clause into Arabic; preserve clause numbers and highlight unclear source terms for reviewer: {source_text}."
How do I train prompt templates for consistent tone across multiple campaigns?
Define a short tone guide per campaign (examples of acceptable phrasing, three approved variants for marketing). Create a base prompt that references the guide and require the model to produce multiple variants. Then capture human-approved outputs to refine prompts iteratively.
What steps should non-Arabic-speaking editors follow to validate AI-assisted Arabic output?
Provide a validation checklist: 1) Confirm named-entities unchanged, 2) Ensure markup/placeholders preserved, 3) Check tone against provided approved variants, 4) Run back-translation to spot meaning drift, and 5) If available, consult a bilingual glossary or translation memory match for critical terms.
