diff --git a/README.md b/README.md
index 417f5a0..f6a4898 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ A comprehensive Python project template designed specifically for research devel
 - **Comprehensive Linting & Type Checking**: Pre-configured with mypy, isort, pylint, black, pydocstyle, and flake8
 - **Pre-commit Hooks**: Automated code quality checks before each commit
 - **CI/CD Integration**: GitHub Actions workflow for continuous integration on PRs
-- **Experiment Tracking**: Ready-to-use configuration for Hydra and Weights & Biases (W&B)
+- **Experiment Tracking**: Ready-to-use configuration for Hydra and Weights & Biases (W&B) (optional dependencies)
 
 ## Directory Structure
 
@@ -79,6 +79,8 @@ powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
 
 This template is designed to support reproducible research experiments using [Hydra](https://hydra.cc/) for configuration management and [Weights & Biases (W&B)](https://wandb.ai/) for experiment tracking.
 
+> **Note**: Hydra and W&B are included as dependencies in this template for convenience, but they are **optional**. You can remove them from `pyproject.toml` if you don't need experiment tracking or configuration management features.
+
 #### Basic Experiment Execution
 
 Place your experiment scripts in the `experiments/` directory:
@@ -276,18 +278,24 @@ The GitHub Actions workflow (`.github/workflows/pr-monitor.yml`) automatically r
 
 ## Adding New Dependencies
 
-Use uv to add new dependencies:
+Use uv to add new dependencies to your project. The `uv add` command automatically installs the package and updates `pyproject.toml`:
 
 ```bash
 # Add a runtime dependency
-uv pip install package-name
-# Then add it to pyproject.toml under [project.dependencies]
+uv add package-name
 
 # Add a development dependency
-uv pip install package-name
-# Then add it to pyproject.toml under [project.optional-dependencies.dev]
+uv add --dev package-name
+
+# Add a specific version
+uv add "package-name>=1.0.0"
+
+# Add multiple packages at once
+uv add package1 package2 package3
 ```
 
+After adding dependencies, make sure to commit the updated `pyproject.toml` to keep your project configuration in sync.
+
 ## Configuration Files
 
 All linter and type-checker configurations are located in the `.dev-config/` directory:
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000..4c01bf8
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,45 @@
+"""Utility functions for the research project.
+
+This module contains common utility functions used across experiments.
+"""
+
+from typing import Any, Dict, List
+
+
+def compute_mean(values: List[float]) -> float:
+    """Compute the mean of a list of values.
+
+    Args:
+        values: List of numerical values.
+
+    Returns:
+        The arithmetic mean of the input values.
+
+    Raises:
+        ValueError: If the input list is empty.
+
+    Example:
+        >>> compute_mean([1.0, 2.0, 3.0, 4.0])
+        2.5
+    """
+    if not values:
+        raise ValueError("Cannot compute mean of empty list")
+    return sum(values) / len(values)
+
+
+def validate_config(config: Dict[str, Any]) -> bool:
+    """Validate experiment configuration.
+
+    Args:
+        config: Configuration dictionary to validate.
+
+    Returns:
+        True if configuration is valid, False otherwise.
+
+    Example:
+        >>> config = {"learning_rate": 0.001, "batch_size": 32}
+        >>> validate_config(config)
+        True
+    """
+    required_keys = ["learning_rate", "batch_size"]
+    return all(key in config for key in required_keys)
diff --git a/tests/test_example.py b/tests/test_example.py
index 6660df2..56d9635 100644
--- a/tests/test_example.py
+++ b/tests/test_example.py
@@ -34,7 +34,9 @@ def test_compute_mean_single_value(self) -> None:
 
     def test_compute_mean_empty_list(self) -> None:
         """Test that empty list raises ValueError."""
-        with pytest.raises(ValueError, match="Cannot compute mean of empty list"):
+        with pytest.raises(
+            ValueError, match="Cannot compute mean of empty list"
+        ):
             compute_mean([])