diff --git a/backend/app/routes/ai_personas.py b/backend/app/routes/ai_personas.py
index ed084828..c01cbf9a 100644
--- a/backend/app/routes/ai_personas.py
+++ b/backend/app/routes/ai_personas.py
@@ -466,13 +466,13 @@ def batch_generate_personas():
                     audience_brief=custom_data.get('audience_brief')
                 )
             
-            # Add to the list of tasks to be executed in parallel
+            # Add to the list of tasks to be executed
             generation_tasks.append({
                 'prompt_customization': custom_prompt,
                 'temperature': temperature
             })
             
-        # Generate personas in parallel
+        # Generate personas
         personas = []
         with concurrent.futures.ThreadPoolExecutor(max_workers=min(count, 4)) as executor:
             # Start the generation tasks
@@ -495,7 +495,7 @@ def batch_generate_personas():
                     raise PersonaGenerationError(f"Failed to generate one of the personas: {str(exc)}")
         
         return jsonify({
-            "message": f"Successfully generated {len(personas)} personas in parallel",
+            "message": f"Successfully generated {len(personas)} personas",
             "personas": personas
         }), 200
         
@@ -547,13 +547,13 @@ def batch_generate_and_save_personas():
                     audience_brief=custom_data.get('audience_brief')
                 )
             
-            # Add to the list of tasks to be executed in parallel
+            # Add to the list of tasks to be executed
             generation_tasks.append({
                 'prompt_customization': custom_prompt,
                 'temperature': temperature
             })
         
-        # Generate personas in parallel
+        # Generate personas
         generated_personas = []
         with concurrent.futures.ThreadPoolExecutor(max_workers=min(count, 4)) as executor:
             # Start the generation tasks
@@ -611,7 +611,7 @@ def batch_generate_and_save_personas():
             persona_ids.append(str(persona_id))
         
         return jsonify({
-            "message": f"Successfully generated and saved {len(personas)} personas in parallel",
+            "message": f"Successfully generated and saved {len(personas)} personas",
             "personas": personas,
             "persona_ids": persona_ids
         }), 201
@@ -760,7 +760,7 @@ def batch_generate_summaries():
     Generate comprehensive markdown summaries for multiple personas for download/client review.
     
     This endpoint takes a list of persona IDs, fetches their complete data, and generates
-    detailed summaries using LLM processing. Personas are processed in parallel batches of 10
+    detailed summaries using LLM processing. Personas are processed in batches of 10
     to optimize performance while staying within API limits. No upper limit on persona count.
     
     Request body:
@@ -852,7 +852,7 @@ def batch_generate_summaries():
             batch = personas_data[i:i + batch_size]
             current_app.logger.info(f"Processing batch {i//batch_size + 1}: {len(batch)} personas")
             
-            # Process this batch in parallel
+            # Process this batch
             with concurrent.futures.ThreadPoolExecutor(max_workers=batch_size) as executor:
                 # Submit all tasks for this batch
                 future_to_persona = {
diff --git a/src/components/AIRecruiter.tsx b/src/components/AIRecruiter.tsx
index 31d52f3b..722e59a9 100644
--- a/src/components/AIRecruiter.tsx
+++ b/src/components/AIRecruiter.tsx
@@ -89,7 +89,7 @@ export default function AIRecruiter({ targetFolderId, targetFolderName }: AIRecr
         });
       }
       
-      toast.info("Generating AI personas in parallel", {
+      toast.info("Generating AI personas", {
         description: `Creating ${count} synthetic personas based on your brief. This may take ${estimatedTime}. Please be patient.`,
         duration: 10000
       });
@@ -320,7 +320,7 @@ export default function AIRecruiter({ targetFolderId, targetFolderName }: AIRecr
       {isGenerating && (
         <div className="mb-6">
           <div className="flex justify-between items-center mb-2">
-            <span className="text-sm font-medium">Generating personas in parallel...</span>
+            <span className="text-sm font-medium">Generating personas...</span>
             <span className="text-sm text-muted-foreground">{Math.round(generationProgress)}%</span>
           </div>
           <Progress value={generationProgress} className="h-2" />
diff --git a/src/components/ai-recruiter/AIRecruiterForm.tsx b/src/components/ai-recruiter/AIRecruiterForm.tsx
index 70b89509..66ceb4fd 100644
--- a/src/components/ai-recruiter/AIRecruiterForm.tsx
+++ b/src/components/ai-recruiter/AIRecruiterForm.tsx
@@ -438,7 +438,7 @@ export default function AIRecruiterForm({ onSubmit, isGenerating }: AIRecruiterF
           </Button>
           {isGenerating && (
             <div className="text-xs text-muted-foreground mt-2">
-              Generating multiple personas in parallel. This may take 1-2 minutes...
+              Generating personas. This may take 1-2 minutes...
             </div>
           )}
         </div>
diff --git a/src/utils/personaGenerator.ts b/src/utils/personaGenerator.ts
index 21e8f25e..df6e238a 100644
--- a/src/utils/personaGenerator.ts
+++ b/src/utils/personaGenerator.ts
@@ -5,7 +5,7 @@ import { aiPersonasApi, personasApi, foldersApi } from "@/lib/api";
  * Generate synthetic personas using the AI endpoint
  * Using a two-stage approach: 
  * 1. Generate basic profiles in one call
- * 2. Expand each profile into a full persona in parallel
+ * 2. Expand each profile into a full persona
  * 
  * @param brief Audience brief to guide persona generation
  * @param researchObjective Optional research objective to focus persona goals and scenarios
@@ -28,7 +28,7 @@ export async function generateSyntheticPersonas(
   console.log(`🔄 generateSyntheticPersonas using model: ${llmModel || 'gemini-2.5-pro'}`);
   
   try {
-    // We'll use the two-stage approach which leverages parallel processing
+    // We'll use the two-stage approach
     console.log(`Generating ${count} synthetic personas using two-stage approach...`);
     
     // First, check if the brief is too short to be useful