diff --git a/q4-scalability/README.md b/q4-scalability/README.md
index 20aa904..c8307f5 100644
--- a/q4-scalability/README.md
+++ b/q4-scalability/README.md
@@ -19,39 +19,38 @@
                      ▼                                             ▼
        [ Ghaymah In-Memory Cache ]                    [ Ghaymah Stateful Workloads ]
       (Redis Cluster for Sessions)                 (StatefulSet + Ghaymah Block Storage)
-2. Container Sizing & Capacity Calculations
-Target Throughput: 15,000 req/s
+```
 
-Max Throughput per Container: 500 req/s
+---
 
-Base Container Requirement:
+## 2. Container Sizing & Capacity Calculations
 
-Base Containers = 15,000 / 500 = 30 containers
+- **Target Throughput:** 15,000 req/s
+- **Max Throughput per Container:** 500 req/s
+- **Base Container Requirement:**
+  - Base Containers = 15,000 / 500 = **30 containers**
 
-Safety Margin Buffer (30% Overhead):
+- **Safety Margin Buffer (30% Overhead):**
+  - **Option A (Adding 30% extra capacity):** 30 x 1.30 = **39 containers**
+  - **Option B (Targeting 70% max utilization per container):** 15,000 / 350 = 42.85 -> **43 containers**
 
-Option A (Adding 30% extra capacity): 30 x 1.30 = 39 containers
+**Deployment Recommendation:** Set initial HPA baseline to **40 replicas** with auto-scaling limits between **39 to 60 containers**.
 
-Option B (Targeting 70% max utilization per container): 15,000 / 350 = 42.85 -> 43 containers
+---
 
-Deployment Recommendation: Set initial HPA baseline to 40 replicas with auto-scaling limits between 39 to 60 containers.
+## 3. Cold Start Mitigation Strategy for New Containers
 
-3. Cold Start Mitigation Strategy for New Containers
 To eliminate container startup latency during burst auto-scaling events:
+1. **Lightweight Container Images:** Use minimal multi-stage Docker builds (Alpine/Distroless) to reduce pull and extraction time.
+2. **Kubernetes Readiness & Startup Probes:** Separate heavy initialization from readiness checks so traffic is routed only when application memory is warm.
+3. **Pre-warmed Buffer Capacity:** Configure HPA with a conservative scale-down stabilization window and preemptive predictive scaling.
+4. **Lazy Initialization:** Defer non-critical background module loading until after the main web server accepts requests.
 
-Lightweight Container Images: Use minimal multi-stage Docker builds (Alpine/Distroless) to reduce pull and extraction time.
+---
 
-Kubernetes Readiness & Startup Probes: Separate heavy initialization from readiness checks so traffic is routed only when application memory is warm.
+## 4. Ghaymah Block Storage for Stateful Workloads
 
-Pre-warmed Buffer Capacity: Configure HPA with a conservative scale-down stabilization window and preemptive predictive scaling.
-
-Lazy Initialization: Defer non-critical background module loading until after the main web server accepts requests.
-
-4. Ghaymah Block Storage for Stateful Workloads
 Ghaymah Block Storage provides low-latency, high-IOPS persistent storage for stateful applications (Databases, Message Queues):
-
-PersistentVolumeClaims (PVC): Dynamically provisions dedicated block volumes attached directly to Kubernetes StatefulSet pods.
-
-Data Consistency & Isolation: High-performance storage isolated per database instance (e.g., PostgreSQL / MongoDB data directories).
-
-Snapshot & Disaster Recovery: Enables automated point-in-time volume snapshots without service interruption.
+1. **PersistentVolumeClaims (PVC):** Dynamically provisions dedicated block volumes attached directly to Kubernetes StatefulSet pods.
+2. **Data Consistency & Isolation:** High-performance storage isolated per database instance (e.g., PostgreSQL / MongoDB data directories).
+3. **Snapshot & Disaster Recovery:** Enables automated point-in-time volume snapshots without service interruption.