In recent years, Generative AI has revolutionized the way we create, translate, and manage content across different languages. Terms like Generative AI, LLM, and Agentic AI are heard more and more often, but it isn’t always clear what they mean or how they differ from one another. In this article, we clarify these concepts, examine their application in translation and multilingual content, and share practical recommendations so companies can harness their potential safely and responsibly.

What is Generative AI?

Definition:

An AI system whose primary function is to generate content (text, images, software, audio, or video) in response to an instruction or prompt.

Main features:

Generative AI helps us create content, whether it’s written text or images and video. Some other features it has are as follows:

• Answer questions: We can have a conversation by asking questions and getting answers using natural language and in multiple languages.
• Complete texts: We can start a text and ask the AI to finish it.
• Generate summaries: can summarize very long content to the length we request.
• Content writing: we can ask them to prepare the outline for an article or to write the first draft of the article itself.
• Sentiment analysis: it can analyze a text or content and identify the sentiments or emotions expressed in it.

Current examples of Generative AI:

• ChatGPT:(OpenAI)
• Co-pilot:(Microsoft)
• Gemini: (Google)
• Claude: (Anthropic)
• Meta AI:(Meta)

What is an LLM?

1. 1. Technical definition: language models trained on large amounts of text and millions of parameters, based on transformer architectures.
   2. Main features: scale, training data, transfer learning capabilities, and contextual understanding.
   3. Examples of current LLMs (some generative AI systems are also LLMs at the same time): GPT‑4, PaLM, LLaMA, Claude

What is the difference between Generative AI and LLMs?

• 1. Relationship between generative AI and LLMs: LLMs are a subset of generative AI focused on language; they are the engines of many generative AI applications.
  2. Main differences:
     1. Generative AI includes models that create text, images, audio, and video.
     2. LLMs focus solely on language; their size (number of parameters and data) is what defines the adjective “large.”
     3. Some generative models are not LLMs (e.g., Midjourney or DALL·E) and use other architectures to generate images.
     4. Recommendation for implementing LLMs responsibly: always with human evaluation.

What is agentive AI and how does it differ from generative AI and LLMs?

Definition:

Agentic AI is a system of agents capable of planning and autonomously carrying out complex, specialized tasks.

Main differences:

1. 1. Autonomy and function
   2. Interaction model: Generative AI and LLMs are reactive (they act when given an instruction, i.e., a prompt), whereas agentive AI is proactive and decides when and how to act.
   3. Tools and results: Generative AI and LLMs deliver content that a human must review or implement; agentive AI not only generates content (which also needs to be reviewed) but turns it into concrete actions, using tools and APIs to carry out tasks. It connects to your computer’s tools and folders to access information and perform tasks.
   4. Relationship between agentic AI and generative AI: They are not opposing technologies or different evolutionary stages, but complementary approaches. Agentive AI integrates generative models like LLMs to generate text, while using its autonomous capabilities to decide what to do. For example, an agent can plan a marketing campaign (agentive AI) and use a LLM to write the messages and create the images (generative AI or LLMs).

What is an LLM for? Applications and use cases

1. 1. Text and content generation: chatbots, automatic writing, storytelling.
   2. Summary, classification, and information extraction.
   3. Questions and answers, virtual assistants, and sentiment analysis.
   4. Translation and other tasks related to language services.

How are LLMs used in translation and multilingual content?

1. 1. LLM as a translation tool
   2. Comparison with Neural Machine Translation (NMT): NMT remains faster and more accurate in specialized domains, but LLMs can offer more natural translations in some areas.
   3. Advantages and limitations: performance by language with many or few resources (at LocalizationLab we have run tests in different languages and the results vary greatly). Languages with more content and more practice yield better results.
   4. Risks: terminology, context, hallucinations, fabrication of data and content. It is important to ensure that the text is written properly, following the required linguistic standards, and that it is a faithful translation of the original. This last point is especially important, since, unlike machine translation, LLMs can introduce inventions or hallucinations.

Conclusion and considerations for businesses

Opportunities that LLMs offer for businesses in terms of translation and multilingual content creation: greater efficiency and personalization.

LLMs can help with daily tasks of writing, reviewing, and translating content in multiple languages, but we must be aware of the limitations and risks involved. If we want to publish client content in another language, it’s important to have a native speaker review it.

If what we want is help drafting content, reviewing it, or creating a first draft in another language, LLMs can be useful and help us be more efficient. Nevertheless, you should always review the content and make any necessary changes to ensure it is accurate and personalized.

Additionally, it should be noted that LLMs do not use translation memories or glossaries, as computer-assisted translation and neural machine translation software do. Therefore, there may be errors in specialized and proprietary terminology.

Risks and precautions: biases, hallucinations, and privacy.

LLMs do not incorporate terminology or translation memories unless the user uploads the content when drafting the prompt. Therefore, there may be terminological and glossary errors that we will need to review or use other AI tools to verify the glossary and terminology.

In addition, hallucinations (false information) and the invention of data and content are common. It is important to ensure that the text is written properly, following the required linguistic standards, and that it is a faithful translation of the original.

If we use a free LLM, we have to keep in mind that the content we upload can be used to train the models and may end up with other companies. It is important to read the security and privacy specifications of each LLM before uploading sensitive or confidential content.

Recommendations for implementing LLMs responsibly: human evaluation, quality testing, and gradual adaptation.

To make good use of AI and be more efficient, we recommend the following:

• Write a detailed and complete prompt with all the information.
• Read the privacy and security information before uploading content.
• Always review the content in your own language and have it reviewed in other languages by an expert linguist.
• Use other AI tools to verify the result.

Consulted and referenced sources

• 1. Stanford University – AI Demystified
  2. HPE (Hewlett Packard Enterprise), “What is a Large Language Model (LLM)?”
  3. OTRS Blog – “Agential AI vs.”Generative AI: Comparison and Best Practices Explain that generative AI creates content from human instructions and uses LLMs to generate results with a natural sound. Defines agentic AI as autonomous, capable of planning and executing actions, and emphasizes that autonomy is the key difference.
  4. Databricks Blog – “Agentic AI vs Generative AI:”Comparing Autonomy, Workflows, and Use Cases Provides a comparative table that highlights the differences in autonomy, function, tool usage, and risks, and explains that generative AI is suitable for specific content-related tasks, while agentive AI automates the entire workflow.
  5. Coursera – “Generative AI vs.Large Language Models:”What’s the Difference?” Clearly define that generative AI encompasses any system capable of generating content, while an LLM is a model specialized in text.
  6. Microsoft Learn, “Using artificial intelligence and large language models for translation” – Explains that generative AI is based on models like LLMs and that these are capable of generating text combinations in various languages; details the advantages and challenges of using them for translation and compares NMT with LLMs.
  7. NYU – Generative AI and Large Language Models (LLMs):Generative AI
  8. NVIDIA Blog, “What Are Large Language Models Used For?”