You scan a French contract, OCR extracts the text perfectly, and then ask ChatGPT: “Summarize this contract.” The AI responds... by subtly transforming your “force majeure” into “unforeseeable circumstances” and your “resolutory clause” into “termination clause”.
This implicit translation seems trivial, but it can have important consequences. In French law, these terms have specific legal implications that their English equivalents don't always capture. Your analysis, which is technically correct, is potentially becoming approximate.
We have become accustomed to writing our prompts in English. The tutorials are mostly in English, the models seem to be more efficient in this language, and it has become a standard in many organizations.
However, when the AI receives a French document and an English prompt, it tries to harmonize its response with the language of the instruction. The result: an unintended translation that can dilute the original precision. This problem becomes critical in sectors where specialized terminology is essential.
In the medical field, French terms can have specificities that the translation does not always accurately capture. In finance, French accounting standards use terminology that does not necessarily correspond to Anglo-Saxon standards. In law, the concepts of French law sometimes lose their specificity in machine translation.
These approximations, however minor in appearance, can affect the quality of the analysis and the resulting decision-making.
The rule is simple: French document = French prompt. This approach allows AI to focus on pure analysis without implicit translation. Technical terms maintain their original precision, and nuances specific to the language of the document are better preserved.
It is important to note that modern AI models like GPT-4 or Claude are fluent in French and can produce quality analyses in this language.
Identify the main language of your source documents and adapt your prompts accordingly. Create templates in each working language: “Analyze this contract and identify important clauses” rather than “Analyze this contract and identify key clauses”.
For particularly sensitive documents, you can perform a comparative analysis: the first with linguistic consistency, the second with your usual method, in order to assess the differences.
Organizations that adopt this approach generally report improved terminological accuracy and the contextual relevance of their analyses. This method also tends to reduce the verification time required after the automatic analysis.
The initial investment in adapting the prompts often results in a significant improvement in the quality of results.
In a context where AI is becoming central to document processing, linguistic coherence represents an important factor in analytical quality. This simple approach can significantly improve the reliability of your AI analyses.
Linguistic consistency is not an optional technical refinement, but a key factor in obtaining accurate and usable analyses.
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