It is becoming increasingly common to communicate with a database, search engine, or AI tool in everyday language. You ask a question, and the system interprets it using advanced language models. You receive a ready-made answer based on the meaning of words, their synonyms and the context of your question.
Even in scholarly literature databases, you can increasingly use natural language and search directly with your research question without first identifying keywords.
This makes searching easier, more intuitive, and more user-friendly.
However, using natural language also has risks:
Everyday language can be unclear or ambiguous and may be misinterpreted by a language model.
Lack of transparency: you don’t know exactly how the system interprets your question.
Questions can be insufficiently specific, leading to broad results. The system won’t necessarily point this out.
Many language models are trained in standard English and may struggle with other languages or jargon.
In short, searching in everyday language is very tempting, but it is important that you also learn to search using keywords and maintain control over the search process.