10+ Prompt Engineering Tricks Every Translator Should Know

10+ prompt engineering tricks for translators using AI. Expert tips to improve translation quality, reduce errors, and save time.
Dec 22 / Alfonso González Bartolessis
Prompt engineering has quickly become a core skill for language professionals working with AI.

Large Language Models can significantly speed up translation workflows, but only when they are guided with clear, well-structured instructions. Without the right prompts, AI output can become inconsistent, overly literal, or even inaccurate, creating more work instead of saving time.

In this article, we explore 10+ practical prompt engineering tricks every language professional should know to get better results from AI-assisted translation.

Rather than relying on generic advice, we gathered the advice from two AI localization experts, Ekaterina Chashnikova and Andrés Romero Arcas, each with hands-on experience using large language models in real professional workflows.

For each topic, we introduce a key challenge that translators face when working with AI, such as controlling terminology, preventing hallucinations, or managing long texts, and then share the perspectives of both experts on how to address it. The result is a balanced, practical guide focused on real translation and localization needs, not theoretical prompt engineering.

Whether you use AI for first drafts, terminology checks, or repetitive translation tasks, these prompt engineering techniques will help you work more efficiently while maintaining quality, consistency, and control.
TABLE OF CONTENTS
  • 1. Define the Translation Goal, Role, and Audience
  • 2. Start Simple: Build Prompts Step by Step
  • 3. Use Examples and Reference Translations to Guide the Model
  • 4. Control Style, Naturalness, and Output Scope
  • 5. Ensure Terminology Consistency Across Translations
  • 6. Design Prompts for Long Texts and Real Translation Workflows
  • 7. Iterate, Revise, and Ask the Model to Improve Itself
  • 8. Prevent Hallucinations and Unwanted Content
  • 9. How TranslaStars Helps Translators Work Smarter with AI

1. Define the Translation Goal, Role, and Audience

Before asking an AI to translate, it’s essential to define what you actually need from the output. Translation is rarely a neutral, one-size-fits-all task: the same source text may require different choices depending on its purpose, target audience, and level of specialization.

Without this context, AI models tend to default to generic translations that may be linguistically correct but unsuitable for real-world use.
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Ekaterina Chashnikova, Trainer and evaluator of generative AI tools, emphasized the importance of keeping the final goal in mind when building a prompt.

A simple instruction may be sufficient if the aim is to obtain a rough draft, but more detailed guidance is required when the output is expected to be close to publication-ready or part of a recurring workflow.
  • Your prompt does not have to be complex or expert-level, but it should always fit your needs. When building a prompt, always keep your goal in mind. What do you want to get as a final result? If you need a draft, you will probably be satisfied with a simple prompt. If you need AI to produce an almost-ready piece and/or take on a step in a repeated task, you will probably need to invest more effort into the prompt and fine-tune it based on the outcome.
If you want to develop skills in AI technologies, grow, and boost your career, Ekaterina will be more than happy to guide you in our IV Edition of the AI Certificate “AI for Translators & Interpreters: Prompt Engineering, Tools and Applications” coming up in January. In this course, she will show you how AI tools work and how to apply them to your everyday work.

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Andrés Romero Arcas, Machine Translation and AI Specialist at Acolad Group, approaches this challenge by formalizing context directly in the prompt. Assigning a clear role to the model helps set the right background.
  • Assign a Role: Always tell the model "who it is" at the beginning of the prompt. It is one of the best practices in prompt engineering for any model, as it helps set the model's background, enabling it to perform the task more effectively.

    Example:
    • Define the Target Audience Explicitly: Specify your target audience, who will read the output. This helps the model draft a response more tailored to the translation's end users.

      Example:
    • Specify the Translation Goal: Explain the purpose of the translation, which may differ from the source content.

      Example:

    2. Start Simple: Build Prompts Step by Step

    One of the most common mistakes when working with AI is trying to design the “perfect” prompt from the start. Overly complex prompts can become difficult to manage, contradictory, or time-consuming to refine.

    In professional translation, effectiveness often comes from starting with a clear, simple instruction and gradually adding constraints only when they are truly needed.
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    Ekaterina recommends beginning with a minimal prompt and improving it incrementally based on the output. This approach makes it easier to identify what works and what doesn’t, while avoiding unnecessary complexity.
    • If you're not sure how to start, start simple and build the prompt step by step.


    • Long prompts can get repetitive and even self-contradictory. Use them with care.
    While Andrés strongly values structure, he also warns against replacing clarity with verbosity. Instead of long explanations, he suggests using concise instructions and, when needed, a well-chosen example to set expectations.
    • Use Examples: One example is sometimes more effective than a paragraph of instructions. It helps you set the tone and register more easily.

      Example:

    3. Use Examples and Reference Translations to Guide the Model

    Providing examples or reference translations is one of the most effective ways to guide AI models toward the desired output.

    Translators often need the model to match a specific style, tone, or terminology, and simply giving instructions in words may not be enough.

    Examples help the AI “see” what kind of translation is expected and reduce the risk of misinterpretation.
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    Ekaterina emphasizes that including reference translations can dramatically improve results.
    Even a single example can help the model understand formal aspects, tone, or preferred word choices.
    • Provide references if you can. LLMs can be great at analyzing formal aspects of texts. A simple "translate from language A to language B" instruction combined with a reference translation can make wonders.
    Speaking of LLMs, remember that the future needs human experts to train AI, and now you have the chance to become one of them. If you want to be equipped with theoretical understanding and hands-on skills to effectively evaluate, guide, and train LLMs, we’ve got you covered.

    Enroll in our expert course “Certificate in AI Training: LLM Specialization for Language Professionals” hosted by Almira Zainutdinova, now available on demand.



    As we’ve seen above, Andrés advocates using examples strategically to set the tone and register of the translation. Rather than relying on long paragraphs of instructions, one clear example often conveys the required style and structure more effectively. This approach not only saves time but also increases consistency across multiple outputs.

    4. Control Style, Naturalness, and Output Scope

    Even when the translation is technically correct, AI can produce results that are too literal, inconsistent in tone, or include unnecessary explanations.

    Controlling style, naturalness, and the scope of output is essential for language professionals who want AI to produce content that is not only accurate but also appropriate for its intended audience.
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    Ekaterina stresses the importance of giving explicit instructions about style and expected output. If the model’s response does not meet expectations, it often signals that the instructions were ambiguous or incomplete, highlighting the need for clearer, more precise prompts.
    • LLMs have evolved over the past 3 years, but it's still best to treat them like students or interns and explain the task very explicitly and clearly.


    • If the outcome is unsatisfactory, ask yourself whether you've given the right instructions. If you were given the same instructions and nothing else, what is the probability that you would have fulfilled the task?
    Andrés approaches this challenge by including directives in the prompt to control fluency and prevent unnecessary additions.

    Clear guidance on output scope helps avoid post-editing work and keeps translations consistent with the intended style.
    • Control Literal vs. Natural Output: To prevent LLMs from producing literal translations, include an instruction to improve fluency in the prompt.

      Example:

    • Explicitly Forbid Added Information: Add it to your prompt if you want the LLM to respond only with the translation, without explanations or additional commentary.

      Example: 

    5. Ensure Terminology Consistency Across Translations

    Maintaining consistent terminology is critical for professional translations, especially in technical, legal, or marketing content. Inconsistent word choices can confuse readers, dilute brand messaging, and reduce overall translation quality.

    AI models don’t automatically apply consistent terminology, so explicit guidance is necessary.
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    Ekaterina points out that the longer the text, the higher the risk of inconsistencies or errors. She recommends breaking content into manageable segments while monitoring for consistent terminology across the pieces.
    • The longer the text, the higher the probability of errors, hallucinations, or simply an unsatisfactory outcome. Try building prompts that work well when you feed your text to the AI piece by piece (you will need to pay attention to the consistency of the LLM responses).
    Andrés emphasizes the importance of forcing terminology compliance through glossaries or explicit instructions.
    By providing the model with a clear list of terms and their approved translations, the AI is more likely to maintain accuracy and consistency.
    • Force Terminology Compliance: Don't rely on the model to keep terminology consistent across translations.

      Example: 

    6. Design Prompts for Long Texts and Real Translation Workflows

    Translating long texts with AI introduces unique challenges, including increased risk of errors, hallucinations, and inconsistent terminology. Effective prompt design and workflow planning are essential to ensure quality while leveraging AI for efficiency.

    As we’ve seen from their suggestions above, Ekaterina and Andrés advise breaking long texts into smaller segments to reduce errors and make outputs more manageable, and also structuring prompts with clear instructions and, where possible, using examples or reference points to guide the AI across longer content.

    By designing prompts as part of an organized workflow, translators can scale AI-assisted work without sacrificing quality.
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    In 2024, more than 65% of companies globally already say they regularly use generative AI for business functions, and many more plan to adopt it soon. Learn how to master the exact skills needed to reliably steer LLMs toward professional-grade outputs with Andrés and his expert course "Prompt Engineering, Evaluation, & Refinement", coming up next February.

    7. Iterate, Revise, and Ask the Model to Improve Itself

    Even with carefully crafted prompts, the first output from an AI model is rarely perfect. Iteration and revision are essential steps to ensure translations meet professional standards, save time on post-editing, and maintain consistency across projects.
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    Ekaterina highlights the value of using built-in features like “regenerate response” to refine AI outputs quickly. She also recommends asking the AI for guidance when stuck on prompt design, treating the model as a collaborative tool rather than a black box.
    • When you're stuck in the process of building the ideal prompt, ask AI for help. It knows a lot about prompt engineering, however awkward it may sound.


    • Most generative AI tools with a chatbot interface (like ChatGPT) have a small button to regenerate the response using the same prompt (it can also be called "Retry"). Clicking this button sends a signal to the LLM that you are not satisfied with the response. Sometimes it works better than tailoring the prompt, so do not ignore it.
    Andrés emphasizes requesting alternative versions and including self-revision instructions within the prompt. By asking the AI to review its own translation for unnatural phrasing or inconsistencies, language professionals can obtain outputs that are closer to the final desired result.
    • Ask for Alternative Versions. So you have more options to choose the most appropriate one.

      Example:

      • Ask for Self-Revision: Include it at the end of your prompt so that the LLM reviews its response before delivering the final output.

        Example:

      8. Prevent Hallucinations and Unwanted Content

      AI translations can sometimes include information that isn’t in the source text or make assumptions that lead to errors. Controlling hallucinations and unwanted content is essential for maintaining accuracy and trustworthiness in professional translations.
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      Ekaterina notes that the risk of hallucinations increases with longer texts and more complex prompts. But if building a prompt becomes more time-consuming than the translation itself, it may be better to use traditional methods rather than force AI to handle the task.
      • When you ask AI to translate, your goal is probably to save time. So, if building the right prompt is taking too much time because of unsatisfactory outcomes, the right way might be to ditch AI and use another translation method. AI is just a tool, and sometimes it's not right for the job.
      Andrés recommends explicitly limiting the model’s freedom in the prompt. Making ambiguities clear and instructing the AI not to add any information outside the source text helps reduce hallucinations
      • Make Ambiguity Explicit: This provides the LLM with additional context to reason about the source content, which often leads to more accurate translations.

        Example:

      9. How TranslaStars Helps Translators Work Smarter with AI

      Prompt engineering is no longer an optional skill for language professionals. It is a practical necessity for producing high-quality, consistent, and audience-appropriate translations with AI.

      By defining clear goals, providing examples, controlling style, and iterating intelligently, language professionals can harness AI as a powerful support tool rather than a source of errors or inefficiency.

      For those looking to deepen their expertise further, the III edition of our Master in AI and Innovation for Localization is designed to equip language professionals with cutting-edge AI tools and techniques to enhance their localization, translation, and content creation skills.



      Additionally, with the VI edition of the Localization Management Program, you’ll have the chance to learn the knowledge and tools needed to manage large-scale localization projects efficiently, combining human expertise and AI capabilities.
      Get your set and enroll now.



      By leveraging these programs, language professionals can not only improve individual outputs but also optimize entire workflows, ensuring higher quality, faster delivery, and smarter use of AI across their translation projects.

      A huge thanks to our experts, Ekaterina and Andrés, for providing their invaluable suggestions and insights about the topic.
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