diff --git a/docs/prompt_hacking/defensive_measures/overview.md b/docs/prompt_hacking/defensive_measures/overview.md
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--- a/docs/prompt_hacking/defensive_measures/overview.md
+++ b/docs/prompt_hacking/defensive_measures/overview.md
@@ -4,7 +4,6 @@ sidebar_position: 0
 
 # 🟢 Overview
 
-Preventing prompt injection can be extremely difficult, and there exist few robust defenses against it(@crothers2022machine)(@goodside2021gpt). However, there are some commonsense
-solutions. For example, if your application does not need to output free-form text, do not allow such outputs. There are many different ways to defend a prompt. We will discuss some of the most common ones here.
+Preventing prompt injection can be extremely difficult, and there exist few robust defenses against it(@crothers2022machine)(@goodside2021gpt). However, there are some commonsense solutions. For example, if your application does not need to output free-form text, do not allow such outputs. There are many different ways to defend a prompt. We will discuss some of the most common ones here.
 
-This chapter covers additional commonsense strategies like filtering out words. It also covers prompt improvement strategies (instruction defense, post-prompting, different ways to enclose user input, and XML tagging). Finally, we discuss using an LLM to evaluate output and some more model specific approaches. 
+This chapter covers additional commonsense strategies like filtering out words. It also covers prompt improvement strategies (instruction defense, post-prompting, different ways to enclose user input, and XML tagging). Finally, we discuss using an LLM to evaluate output and some more model specific approaches.
diff --git a/i18n/fr/docusaurus-plugin-content-docs/current/prompt_hacking/defensive_measures/_category_.json b/i18n/fr/docusaurus-plugin-content-docs/current/prompt_hacking/defensive_measures/_category_.json
new file mode 100644
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+++ b/i18n/fr/docusaurus-plugin-content-docs/current/prompt_hacking/defensive_measures/_category_.json
@@ -0,0 +1,8 @@
+{
+  "label": "🟢 Mesures défensives",
+  "position": 50,
+  "link": {
+    "type": "generated-index",
+    "description": "Le hacking, mais pour le PE"
+  }
+}
diff --git a/i18n/fr/docusaurus-plugin-content-docs/current/prompt_hacking/defensive_measures/overview.md b/i18n/fr/docusaurus-plugin-content-docs/current/prompt_hacking/defensive_measures/overview.md
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+++ b/i18n/fr/docusaurus-plugin-content-docs/current/prompt_hacking/defensive_measures/overview.md
@@ -0,0 +1,9 @@
+---
+sidebar_position: 0
+---
+
+# 🟢 Aperçu
+
+Prévenir l'injection de prompt (prompt injection) peut être extrêmement difficile, et il existe peu de défenses robustes contre cela(@crothers2022machine)(@goodside2021gpt). Cependant, certaines solutions de bon sens existent. Par exemple, si votre application n'a pas besoin de produire du texte libre, ne permettez pas de tels résultats. Il existe de nombreuses manières différentes de défendre un prompt. Nous discuterons ici de certaines des plus courantes.
+
+Ce chapitre couvre des stratégies supplémentaires de bon sens comme filtrer les mots. Il traite également des stratégies d'amélioration de prompt (défense par instruction, post-prompting, différentes façons d'encadrer les entrées utilisateur, et le balisage XML). Enfin, nous discutons de l'utilisation d'un LLM pour évaluer la sortie et de quelques approches plus spécifiques au modèle.