diff --git a/ai_prompt.txt b/ai_prompt.txt
index 6e178e4..db02397 100644
--- a/ai_prompt.txt
+++ b/ai_prompt.txt
@@ -1,8 +1,3 @@
-Here's a comprehensive AI prompt that will guide you through the entire process of analyzing CSV data, migrating to a database, and building a Python application to query it:
-
-## **Complete AI Prompt for CSV to Database Migration & Python App Development**
-
-```text
 I need to analyze CSV files containing school scheduling data and create a complete Python database system. Here's what I need:
 
 ## **PROJECT OVERVIEW**
diff --git a/scheduler_bots/__pycache__/database.cpython-314.pyc b/scheduler_bots/__pycache__/database.cpython-314.pyc
index 7b63e43..1f1e1fc 100644
Binary files a/scheduler_bots/__pycache__/database.cpython-314.pyc and b/scheduler_bots/__pycache__/database.cpython-314.pyc differ
diff --git a/scheduler_bots/database.py b/scheduler_bots/database.py
index 56fffce..24d8b72 100644
--- a/scheduler_bots/database.py
+++ b/scheduler_bots/database.py
@@ -892,6 +892,14 @@ class SchoolScheduleDB:
         # Check if it looks like a name with multiple capitalized words (Russian or English)
         # Teacher names typically have 2-4 words with capitalized first letters
         words = text.split()
+        
+        # Handle compound names like "Name1 / Name2"
+        if '/' in text:
+            parts = [part.strip() for part in text.split('/')]
+            for part in parts:
+                if self._is_likely_teacher_name_simple(part):
+                    return True
+        
         if len(words) < 2 or len(words) > 4:
             return False
         
@@ -957,6 +965,7 @@ class SchoolScheduleDB:
     def update_teachers_from_cheat_sheet(self, teachers_csv_path):
         """Update the teachers table from the Teachers.csv file"""
         import csv
+        import re
         
         with open(teachers_csv_path, 'r', encoding='utf-8') as file:
             reader = csv.reader(file)
@@ -966,23 +975,51 @@ class SchoolScheduleDB:
         unique_teachers = set()
         
         # Process the CSV to extract teacher names
+        # Teacher names appear in positions after subjects and before group info
+        for row in rows:
+            # Process the row in triplets (subject, teacher, group_info) pattern
+            j = 0
+            while j < len(row) - 2:
+                # Look for a subject-like cell followed by a teacher-like cell
+                subject_cell = str(row[j]).strip() if j < len(row) else ""
+                teacher_cell = str(row[j + 1]).strip() if j + 1 < len(row) else ""
+                group_cell = str(row[j + 2]).strip() if j + 2 < len(row) else ""
+                
+                # Check if middle cell is likely a teacher name (between subject and group)
+                if (subject_cell and self._is_likely_subject_name_simple(subject_cell) and 
+                    teacher_cell and self._is_likely_teacher_name_enhanced(teacher_cell) and
+                    group_cell and self._is_likely_group_identifier(group_cell)):
+                    
+                    # Handle composite teacher names like "Name1 / Name2"
+                    if '/' in teacher_cell:
+                        parts = [part.strip() for part in teacher_cell.split('/')]
+                        for part in parts:
+                            if self._is_likely_teacher_name_enhanced(part):
+                                unique_teachers.add(part)
+                    else:
+                        unique_teachers.add(teacher_cell)
+                
+                # Also check if the current cell itself might be a teacher name
+                current_cell = str(row[j]).strip()
+                if current_cell and self._is_likely_teacher_name_enhanced(current_cell):
+                    unique_teachers.add(current_cell)
+                
+                j += 1  # Move to next cell
+        
+        # Special handling for teacher names that appear in combination patterns like "First Last / Other Teacher"
         for row in rows:
             for cell in row:
-                cell = cell.strip()
-                # Check if the cell looks like a teacher name (Russian/English name format)
-                if self._is_likely_teacher_name_enhanced(cell):
-                    unique_teachers.add(cell)
-                
-                # Also check for names that might be combined with "/" (like "Name1 / Name2")
-                if '/' in cell:
-                    parts = [part.strip() for part in cell.split('/')]
+                cell_str = str(cell).strip()
+                if '/' in cell_str:
+                    parts = [part.strip() for part in cell_str.split('/')]
                     for part in parts:
+                        part = part.strip()
                         if self._is_likely_teacher_name_enhanced(part):
                             unique_teachers.add(part)
         
         # Insert unique teachers into the database
         for teacher_name in unique_teachers:
-            if teacher_name:  # Make sure it's not an empty string
+            if teacher_name and len(teacher_name) > 2:  # Make sure it's not an empty string or very short
                 self.cursor.execute(
                     "INSERT OR IGNORE INTO teachers (name, email, phone) VALUES (?, NULL, NULL)",
                     (teacher_name,)
diff --git a/scheduler_bots/school_schedule.db b/scheduler_bots/school_schedule.db
index 2d1d507..ae2b547 100644
Binary files a/scheduler_bots/school_schedule.db and b/scheduler_bots/school_schedule.db differ