diff --git a/modules/source/01_setup/setup_dev.py b/modules/source/01_setup/setup_dev.py
index 0db553f6..2d771746 100644
--- a/modules/source/01_setup/setup_dev.py
+++ b/modules/source/01_setup/setup_dev.py
@@ -456,6 +456,13 @@ Now let's test your configuration functions!
 Once you implement both functions above, run this cell to test them:
 """
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Personal Information Configuration
+
+This test validates your `personal_info()` function implementation, ensuring it returns properly formatted developer information for system attribution and collaboration.
+"""
+
 # %%
 def test_unit_personal_info_basic():
     """Test personal_info function implementation."""
@@ -493,6 +500,13 @@ def test_unit_personal_info_basic():
 # Run the test
 test_unit_personal_info_basic()
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: System Information Query
+
+This test validates your `system_info()` function implementation, ensuring it accurately detects and reports hardware and software specifications for performance optimization and debugging.
+"""
+
 # %%
 def test_unit_system_info_basic():
     """Test system_info function implementation."""
diff --git a/modules/source/02_tensor/tensor_dev.py b/modules/source/02_tensor/tensor_dev.py
index 2100dd12..43f02d39 100644
--- a/modules/source/02_tensor/tensor_dev.py
+++ b/modules/source/02_tensor/tensor_dev.py
@@ -820,6 +820,13 @@ Congratulations! You've successfully implemented the core Tensor class for TinyT
 **Ready for the next challenge?** Let's add the mathematical functions that make neural networks powerful!
 """
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Tensor Creation
+
+This test validates your `Tensor` class constructor, ensuring it correctly handles scalars, vectors, matrices, and higher-dimensional arrays with proper shape detection.
+"""
+
 # %%
 def test_unit_tensor_creation():
     """Comprehensive test of tensor creation with all data types and shapes."""
@@ -841,6 +848,13 @@ def test_unit_tensor_creation():
 # Run the test
 test_unit_tensor_creation()
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Tensor Properties
+
+This test validates your tensor property methods (shape, size, dtype, data), ensuring they correctly reflect the tensor's dimensional structure and data characteristics.
+"""
+
 # %%
 def test_unit_tensor_properties():
     """Comprehensive test of tensor properties (shape, size, dtype, data access)."""
@@ -864,6 +878,13 @@ def test_unit_tensor_properties():
 # Run the test
 test_unit_tensor_properties()
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Tensor Arithmetic Operations
+
+This test validates your tensor arithmetic implementation (addition, multiplication, subtraction, division) and operator overloading, ensuring mathematical operations work correctly with proper broadcasting.
+"""
+
 # %%
 def test_unit_tensor_arithmetic():
     """Comprehensive test of tensor arithmetic operations."""
diff --git a/modules/source/12_compression/compression_dev.py b/modules/source/12_compression/compression_dev.py
index f21b8237..080f3994 100644
--- a/modules/source/12_compression/compression_dev.py
+++ b/modules/source/12_compression/compression_dev.py
@@ -365,6 +365,13 @@ class CompressionMetrics:
             'dtype': dtype
         }
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Compression Metrics Analysis
+
+This test validates your `CompressionMetrics` class implementation, ensuring it accurately calculates model parameters, memory usage, and compression statistics for optimization analysis.
+"""
+
 # %% nbgrader={"grade": false, "grade_id": "test-compression-metrics", "locked": false, "schema_version": 3, "solution": false, "task": false}
 def test_unit_compression_metrics():
     """Unit test for the CompressionMetrics class."""
@@ -572,6 +579,13 @@ def calculate_sparsity(layer: Dense) -> float:
     return zero_weights / total_weights if total_weights > 0 else 0.0
     ### END SOLUTION 
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Magnitude-Based Pruning
+
+This test validates your pruning implementation, ensuring it correctly identifies and removes the smallest weights while maintaining model functionality and calculating accurate sparsity metrics.
+"""
+
 # %% nbgrader={"grade": false, "grade_id": "test-pruning", "locked": false, "schema_version": 3, "solution": false, "task": false}
 def test_unit_magnitude_pruning():
     """Unit test for the magnitude-based pruning functionality."""
@@ -759,6 +773,13 @@ def quantize_layer_weights(layer: Dense, bits: int = 8) -> Tuple[Dense, Dict[str
     return layer, quantization_info
     ### END SOLUTION 
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Weight Quantization
+
+This test validates your quantization implementation, ensuring it correctly converts FP32 weights to INT8 representation while minimizing accuracy loss and achieving significant memory reduction.
+"""
+
 # %% nbgrader={"grade": false, "grade_id": "test-quantization", "locked": false, "schema_version": 3, "solution": false, "task": false}
 def test_unit_quantization():
     """Unit test for the weight quantization functionality."""
@@ -976,6 +997,13 @@ class DistillationLoss:
         probs = self._softmax(logits)
         return -np.mean(np.sum(labels * np.log(probs + 1e-10), axis=-1)) 
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Knowledge Distillation
+
+This test validates your knowledge distillation implementation, ensuring the student model learns effectively from teacher predictions while maintaining computational efficiency.
+"""
+
 # %% nbgrader={"grade": false, "grade_id": "test-distillation", "locked": false, "schema_version": 3, "solution": false, "task": false}
 def test_unit_distillation():
     """Unit test for the DistillationLoss class."""
@@ -1260,6 +1288,13 @@ def prune_layer_neurons(layer: Dense, keep_ratio: float = 0.7,
     return pruned_layer, pruning_info
     ### END SOLUTION 
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Structured Pruning
+
+This test validates your structured pruning implementation, ensuring it correctly removes entire neurons or channels while maintaining model architecture integrity and computational efficiency.
+"""
+
 # %% nbgrader={"grade": false, "grade_id": "test-structured-pruning", "locked": false, "schema_version": 3, "solution": false, "task": false}
 def test_unit_structured_pruning():
     """Unit test for the structured pruning (neuron pruning) functionality."""
@@ -1590,6 +1625,13 @@ Each compression technique includes comprehensive unit tests:
 This module teaches the essential skills for deploying AI in resource-constrained environments!
 """
 
+# %% [markdown]
+"""
+### 🧪 Unit Test: Comprehensive Compression Comparison
+
+This test validates the complete compression pipeline, comparing different techniques (pruning, quantization, distillation) to analyze their effectiveness and trade-offs in model optimization.
+"""
+
 # %% nbgrader={"grade": false, "grade_id": "test-comprehensive-comparison", "locked": false, "schema_version": 3, "solution": false, "task": false}
 def test_unit_comprehensive_comparison():
     """Unit test for the comparison of different compression techniques."""
@@ -1661,6 +1703,13 @@ def test_unit_comprehensive_comparison():
 # Run the test
 test_unit_comprehensive_comparison()
 
+# %% [markdown]
+"""
+### 🧪 Integration Test: Compression with Sequential Models
+
+This integration test validates that all compression techniques work seamlessly with TinyTorch's Sequential models, ensuring proper layer integration and end-to-end functionality.
+"""
+
 # %%
 def test_compression_integration():
     """Integration test for applying compression to a Sequential model."""
@@ -1693,6 +1742,13 @@ def test_compression_integration():
 
     print("✅ Integration Test Passed: Pruning correctly modified a layer in a Sequential model.")
 
+# %% [markdown]
+"""
+### 🧪 Integration Test: Comprehensive Compression Pipeline
+
+This comprehensive integration test validates the complete compression workflow, applying multiple techniques in sequence and ensuring proper interaction between compression methods and model architectures.
+"""
+
 # %%
 def test_comprehensive_compression_integration():
     """