LLM vs. Data-to-Text: The biggest differences

Large language models (LLMs) such as ChatGPT or Gemini have changed content creation. At the same time, a second approach has been producing millions of texts in companies every day for years: Data-to-Text. Both automate text, but they work in fundamentally different ways. Which approach fits your company, and why does axite combine both?

What is Data-to-Text?

Data-to-Text turns structured data – tables, databases, APIs or a PIM system – into natural language. Every statement in the text can be traced back to a data point. When the data changes, the texts change with it.

What is an LLM?

A large language model is a neural network trained on huge amounts of text. It generates text by predicting the most likely continuation and is steered by prompts. LLMs are flexible, but their knowledge comes from training data, not your product data – so they can hallucinate and don't return the same result every time.

The key differences

When to use which

Use Data-to-Text for large volumes of fact-based texts: product and category pages, travel and real estate descriptions, financial and sports reports. Use an LLM for drafts, ideas, blog posts, social media and chatbots.

axite: Data-to-Text and AI in one platform

axite combines the precision of Data-to-Text with modern AI – without prompting: data as the foundation, AI where it helps, automatic fact-checking, more than 110 languages and your own rules instead of prompts.

Conclusion

LLMs are strong for drafts and creative single texts. For large volumes of precise, data-driven content, Data-to-Text is the more reliable choice – and axite gives you both.