diff --git a/webapp/src/components/home/Home.vue b/webapp/src/components/home/Home.vue
index 1a69e8e56..cdff7a0a0 100644
--- a/webapp/src/components/home/Home.vue
+++ b/webapp/src/components/home/Home.vue
@@ -182,7 +182,7 @@ import DISCOllaboratives from "@/components/simple/DISCOllaboratives.vue";
 import ButtonsCard from "@/components/containers/ButtonsCard.vue";
 import { driver, type DriveStep } from "driver.js";
 import "driver.js/dist/driver.css";
-import { watch } from "vue";
+import { nextTick, watch } from "vue";
 import { useGuideStore } from "@/store/guide";
 
 const router = useRouter();
@@ -253,11 +253,13 @@ driverObj.drive();
 
 watch(
   () => guideStoreTraining.showGuide,
-  (newValue) => {
-    driverObj.destroy();
-    console.log(router.currentRoute.value.path);
-    if (router.currentRoute.value.path == "/") {
+  async (newValue) => {
+    if (driverObj.isActive()) {
+      await driverObj.destroy();
+    }
+    if (router.currentRoute.value.path === "/") {
       driverObj.setSteps(steps);
+      await nextTick();
       driverObj.drive();
     }
   },
diff --git a/webapp/src/components/testing/Testing.vue b/webapp/src/components/testing/Testing.vue
index b10a14784..f81aa8e27 100644
--- a/webapp/src/components/testing/Testing.vue
+++ b/webapp/src/components/testing/Testing.vue
@@ -144,7 +144,7 @@
 <script lang="ts" setup>
 import createDebug from "debug";
 import { List } from "immutable";
-import { computed, ref, onActivated, watch } from "vue";
+import { computed, ref, onActivated, watch, nextTick } from "vue";
 import { RouterLink, useRouter } from "vue-router";
 import { VueSpinner } from "vue3-spinners";
 
@@ -296,7 +296,7 @@ function taskTitle(taskID: string): string | undefined {
 
   return titled.displayInformation.taskTitle;
 }
-// Define the steps for the guide when no user's models are available
+//Define the steps for the guide when no user's models are available
 const modelEmptySteps: DriveStep[] = [
   {
     popover: {
@@ -425,27 +425,23 @@ const evaluationSteps: DriveStep[] = [
 ];
 watch(
   () => guideStoreTesting.showGuide,
-  (newValue) => {
-    driverTesting.destroy();
-    if (router.currentRoute.value.path == "/evaluate") {
-      if (validationStore.step === 0) {
-        if (models.infos.isEmpty()) {
-          driverTesting.setSteps(modelEmptySteps);
-        } else {
-          driverTesting.setSteps(modelSteps);
-        }
+  async (newValue) => {
+    if (driverTesting.isActive()) {
+      await driverTesting.destroy();
+    }
+    if (router.currentRoute.value.path === "/evaluate") {
+      const stepsMap: Record<number, DriveStep[]> = {
+        0: models.infos.isEmpty() ? modelEmptySteps : modelSteps,
+        1: dataSteps,
+        2: evaluationSteps,
+      };
+
+      const steps = stepsMap[validationStore.step] || [];
+      if (steps.length) {
+        driverTesting.setSteps(steps);
+        await nextTick();
         driverTesting.drive();
       }
-      if (validationStore.step !== 0) {
-        if (validationStore.step === 1) {
-          driverTesting.setSteps(dataSteps);
-          driverTesting.drive();
-        }
-        if (validationStore.step === 2) {
-          driverTesting.setSteps(evaluationSteps);
-          driverTesting.drive();
-        }
-      }
     }
   },
 );
diff --git a/webapp/src/components/training/TrainingSteps.vue b/webapp/src/components/training/TrainingSteps.vue
index 205b96df3..ea1f7134b 100644
--- a/webapp/src/components/training/TrainingSteps.vue
+++ b/webapp/src/components/training/TrainingSteps.vue
@@ -26,7 +26,7 @@
 </template>
 
 <script lang="ts" setup>
-import { computed, onMounted, ref, toRaw, watch } from "vue";
+import { computed, nextTick, onMounted, ref, toRaw, watch } from "vue";
 import { useRouter, useRoute } from "vue-router";
 
 import type {
@@ -115,204 +115,366 @@ const unamedDataset = computed<Dataset<DataFormat.Raw[DataType]> | undefined>(
 );
 const trainedModel = ref<Model<DataType>>();
 
+// const guideStoreTraining = useGuideStore();
+
+// //Create driver object
+// const driverTraining = driver({
+//   showProgress: false,
+// });
+// // Define steps for the data section for text and tabular tasks
+// const dataSteps: DriveStep[] = [
+//   {
+//     popover: {
+//       title: "Expected Data Format",
+//       description:
+//         "This section describes the criteria for the data required by the model. Please upload your dataset to proceed with training.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#data-link-download",
+//     popover: {
+//       title: "Get Sample Data",
+//       description:
+//         "Click here to download a sample dataset if you don't have your own. Perfect for testing and experimenting!",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#select-data-bttn",
+//     popover: {
+//       title: "Upload Your Dataset",
+//       description:
+//         "Click here to upload your dataset for model training. Your data stays secure and never leaves your device.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+// ];
+// // Define steps for the data section for timage tasks
+// const dataImageSteps: DriveStep[] = [
+//   {
+//     popover: {
+//       title: "Expected Data Format",
+//       description:
+//         "This section describes the criteria for the data required by the model. Please upload your dataset to proceed with training.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#data-link-download",
+//     popover: {
+//       title: "Get Sample Data",
+//       description:
+//         "Click here to download a sample dataset if you don't have your own. Perfect for testing and experimenting!",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#group-data-bttn",
+//     popover: {
+//       title: "Group Selection",
+//       description:
+//         "Use this option to manually create labeled data groups by selecting images for each category.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#csv-file-bttn",
+//     popover: {
+//       title: "CSV Upload",
+//       description:
+//         "Upload a CSV file to connect your data. Each row should link data points to their respective labels.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#select-data-bttn",
+//     popover: {
+//       title: "Upload Your Dataset",
+//       description:
+//         "Click here to upload your dataset for model training. Your data stays secure and never leaves your device.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+// ];
+// // Define steps for the training section
+// const stepsTraining: DriveStep[] = [
+//   {
+//     popover: {
+//       title: "Training Mode",
+//       description:
+//         "This section allows you to choose how the model will be trained. Select between collaborative or local training modes.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#train-collaboratively-bttn",
+//     popover: {
+//       title: "Collaborative Training",
+//       description:
+//         "Train the model together with other users in a shared session. This requires multiple participants and is ideal for distributed data tasks.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#training-locally-bttn",
+//     popover: {
+//       title: "Local Training",
+//       description:
+//         "Train the model on your own device using only your data. This method is recommended for most users as it ensures privacy and simplicity.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+// ];
+// // Define steps for the evaluation section
+// const stepsEvaluation: DriveStep[] = [
+//   {
+//     popover: {
+//       title: "Model Evaluation",
+//       description:
+//         "This section allows you to evaluate and finalize your trained model by testing and saving it.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#test-model",
+//     popover: {
+//       title: "Test the Model",
+//       description:
+//         "Check how well your model performs by testing it on new data not used during training.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+//   {
+//     element: "#save-model",
+//     popover: {
+//       title: "Save the Model",
+//       description:
+//         "Save your trained model to access it later or continue improving it in future training sessions.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+// ];
+// const stepsCollaboratives: DriveStep[] = [
+//   {
+//     popover: {
+//       title: "Explore DISCOllaboratives",
+//       description:
+//         "Choose from the available machine learning tasks below and train models with your own private data.",
+//       side: "top",
+//       align: "center",
+//     },
+//   },
+// ];
+
+// watch(
+//   () => guideStoreTraining.showGuide,
+//   async (newValue) => {
+//     if (driverTraining.isActive()) {
+//       await driverTraining.destroy();
+//     }
+//     if (
+//       ["/llm_task", "/tinder_dog", "/mnist", "/lus_covid", "/titanic"].includes(
+//         router.currentRoute.value.path,
+//       )
+//     ) {
+//       if (trainingStore.step === 2) {
+//         const steps =
+//           task?.value?.trainingInformation.dataType === "image"
+//             ? dataImageSteps
+//             : dataSteps;
+//         driverTraining.setSteps(steps);
+//         await nextTick();
+//         driverTraining.drive();
+//       } else if (trainingStore.step === 3) {
+//         driverTraining.setSteps(stepsTraining);
+//         await nextTick();
+//         driverTraining.drive();
+//       } else if (trainingStore.step === 4) {
+//         driverTraining.setSteps(stepsEvaluation);
+//         await nextTick();
+//         driverTraining.drive();
+//       }
+//     }
+//   },
+// );
+//
+
 const guideStoreTraining = useGuideStore();
 
-//Create driver object
-const driverOBJ = driver({
+const driverTraining = driver({
   showProgress: false,
 });
-// Define steps for the data section for text and tabular tasks
-const dataSteps: DriveStep[] = [
-  {
+
+const createSteps = (
+  steps:
+    | (
+        | { title: string; description: string; element?: undefined }
+        | { element: string; title: string; description: string }
+      )[]
+    | { element: any; title: any; description: any }[],
+) =>
+  steps.map(({ element, title, description }) => ({
+    ...(element && { element }),
     popover: {
+      title,
+      description,
+      side: "top",
+      align: "center",
+    },
+  }));
+
+const stepsConfig = {
+  data: [
+    {
       title: "Expected Data Format",
       description:
         "This section describes the criteria for the data required by the model. Please upload your dataset to proceed with training.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#data-link-download",
-    popover: {
+    {
+      element: "#data-link-download",
       title: "Get Sample Data",
       description:
         "Click here to download a sample dataset if you don't have your own. Perfect for testing and experimenting!",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#select-data-bttn",
-    popover: {
+    {
+      element: "#select-data-bttn",
       title: "Upload Your Dataset",
       description:
         "Click here to upload your dataset for model training. Your data stays secure and never leaves your device.",
-      side: "top",
-      align: "center",
     },
-  },
-];
-// Define steps for the data section for timage tasks
-const dataImageSteps: DriveStep[] = [
-  {
-    popover: {
+  ],
+  dataImage: [
+    {
       title: "Expected Data Format",
       description:
         "This section describes the criteria for the data required by the model. Please upload your dataset to proceed with training.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#data-link-download",
-    popover: {
+    {
+      element: "#data-link-download",
       title: "Get Sample Data",
       description:
         "Click here to download a sample dataset if you don't have your own. Perfect for testing and experimenting!",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#group-data-bttn",
-    popover: {
+    {
+      element: "#group-data-bttn",
       title: "Group Selection",
       description:
         "Use this option to manually create labeled data groups by selecting images for each category.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#csv-file-bttn",
-    popover: {
+    {
+      element: "#csv-file-bttn",
       title: "CSV Upload",
       description:
         "Upload a CSV file to connect your data. Each row should link data points to their respective labels.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#select-data-bttn",
-    popover: {
+    {
+      element: "#select-data-bttn",
       title: "Upload Your Dataset",
       description:
         "Click here to upload your dataset for model training. Your data stays secure and never leaves your device.",
-      side: "top",
-      align: "center",
     },
-  },
-];
-// Define steps for the training section
-const stepsTraining: DriveStep[] = [
-  {
-    popover: {
+  ],
+  training: [
+    {
       title: "Training Mode",
       description:
         "This section allows you to choose how the model will be trained. Select between collaborative or local training modes.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#train-collaboratively-bttn",
-    popover: {
+    {
+      element: "#train-collaboratively-bttn",
       title: "Collaborative Training",
       description:
         "Train the model together with other users in a shared session. This requires multiple participants and is ideal for distributed data tasks.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#training-locally-bttn",
-    popover: {
+    {
+      element: "#training-locally-bttn",
       title: "Local Training",
       description:
         "Train the model on your own device using only your data. This method is recommended for most users as it ensures privacy and simplicity.",
-      side: "top",
-      align: "center",
     },
-  },
-];
-// Define steps for the evaluation section
-const stepsEvaluation: DriveStep[] = [
-  {
-    popover: {
+  ],
+  evaluation: [
+    {
       title: "Model Evaluation",
       description:
         "This section allows you to evaluate and finalize your trained model by testing and saving it.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#test-model",
-    popover: {
+    {
+      element: "#test-model",
       title: "Test the Model",
       description:
         "Check how well your model performs by testing it on new data not used during training.",
-      side: "top",
-      align: "center",
     },
-  },
-  {
-    element: "#save-model",
-    popover: {
+    {
+      element: "#save-model",
       title: "Save the Model",
       description:
         "Save your trained model to access it later or continue improving it in future training sessions.",
-      side: "top",
-      align: "center",
     },
-  },
-];
-const stepsCollaboratives: DriveStep[] = [
-  {
-    popover: {
+  ],
+  collaboratives: [
+    {
       title: "Explore DISCOllaboratives",
       description:
         "Choose from the available machine learning tasks below and train models with your own private data.",
-      side: "top",
-      align: "center",
     },
-  },
-];
-// Watch for changes in the training step and trigger guided tours accordingly
+  ],
+};
+
+const getStepsForStep = (
+  step: number | undefined,
+  dataType: string | undefined,
+) => {
+  switch (step) {
+    case 2:
+      return dataType === "image"
+        ? createSteps(stepsConfig.dataImage)
+        : createSteps(stepsConfig.data);
+    case 3:
+      return createSteps(stepsConfig.training);
+    case 4:
+      return createSteps(stepsConfig.evaluation);
+    default:
+      return [];
+  }
+};
+
 watch(
   () => guideStoreTraining.showGuide,
-  (newValue) => {
-    if (newValue) {
-      if (trainingStore.step === 1) {
-        console.log("step 1");
-        driverOBJ.destroy();
-        driverOBJ.setSteps(stepsCollaboratives);
-        driverOBJ.drive();
-        guideStoreTraining.toggleGuide();
-      }
-      if (trainingStore.step === 2) {
-        driverOBJ.destroy();
-        if (task?.value?.trainingInformation.dataType === "image") {
-          driverOBJ.setSteps(dataImageSteps);
-        } else {
-          driverOBJ.setSteps(dataSteps);
-        }
-        driverOBJ.drive();
-        guideStoreTraining.toggleGuide();
-      }
-      if (trainingStore.step === 3) {
-        driverOBJ.destroy();
-        driverOBJ.setSteps(stepsTraining);
-        driverOBJ.drive();
-        guideStoreTraining.toggleGuide();
-      }
-      if (trainingStore.step === 4) {
-        driverOBJ.destroy();
-        driverOBJ.setSteps(stepsEvaluation);
-        driverOBJ.drive();
-        guideStoreTraining.toggleGuide();
+  async (newValue) => {
+    if (driverTraining.isActive()) {
+      await driverTraining.destroy();
+    }
+
+    if (
+      ["/llm_task", "/tinder_dog", "/mnist", "/lus_covid", "/titanic"].includes(
+        router.currentRoute.value.path,
+      )
+    ) {
+      const steps = getStepsForStep(
+        trainingStore.step,
+        task?.value?.trainingInformation.dataType,
+      );
+
+      if (steps.length) {
+        driverTraining.setSteps(steps);
+        await nextTick();
+        driverTraining.drive();
       }
     }
   },