diff --git a/README.md b/README.md
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+++ b/README.md
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-# flogging
-[![Documentation Status](https://readthedocs.org/projects/flogging/badge/?version=latest)](https://flogging.readthedocs.io/en/latest/?badge=latest)
-[![Code coverage](https://codecov.io/github/fragiletech/flogging/coverage.svg)](https://codecov.io/github/fragiletech/flogging)
-[![PyPI package](https://badgen.net/pypi/v/flogging)](https://pypi.org/project/flogging/)
-[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
-[![license: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
-
-`flogging` offers nice logging formatting and structured logging. 
\ No newline at end of file
+# Structured Logging Documentation
+
+This module provides structured logging capabilities that allow you to output either human-readable 
+logs or JSON-formatted structured logs. These features are especially useful for debugging in 
+development and for generating easily parseable logs in production environments.
+
+## Overview
+
+The logging setup supports two main modes:
+
+1. **Human-readable logs**: Provides logs in a colored, properly formatted output for local development.
+2. **Structured logs**: Outputs logs as JSON objects, with each log record represented as a single line. This mode is ideal for production environments where logs need to be processed by log aggregation systems.
+
+### Key Functions
+
+- `add_logging_args()`: Adds logging configuration options to an `argparse.ArgumentParser` instance, making it easy to configure logging via command-line arguments.
+- `setup()`: Directly configures logging by specifying the logging level and format (structured or human-readable).
+- `set_context()`: Assigns a custom context to be logged with each message in structured logging mode.
+- `log_multipart()`: Logs large messages by splitting them into chunks and compressing the data.
+
+---
+
+## Usage
+
+### Basic Setup with `setup()`
+
+To initialize logging in your application, call the `setup()` function. You can specify whether to enable structured logging or use the default human-readable format.
+
+```python
+from my_logging_module import setup
+
+# Initialize logging
+setup(level="INFO", structured=False)  # Human-readable format
+
+# Enable structured logging for production
+setup(level="INFO", structured=True)
+```
+
+#### Parameters for `setup()`
+
+- `level`: The logging level (e.g., "DEBUG", "INFO", "WARNING", "ERROR"). This can be a string or an integer.
+- `structured`: A boolean that controls whether structured logging is enabled. Set to `True` for JSON logs.
+- `allow_trailing_dot`: Prevents log messages from having a trailing dot unless explicitly allowed.
+- `level_from_msg`: An optional function to dynamically change the logging level based on the content of the message.
+- `ensure_utf8_streams`: Ensures that `stdout` and `stderr` use UTF-8 encoding.
+
+### Adding Logging Arguments with `add_logging_args()`
+
+You can easily integrate logging configuration options into your command-line interface using `add_logging_args()`. This function automatically adds command-line flags for setting the logging level and format.
+
+#### Command-Line Flags
+
+- `--log-level`: Set the logging verbosity (e.g., "DEBUG", "INFO", "WARNING").
+- `--log-structured`: Enable structured logging (outputs logs in JSON format).
+
+#### Environment Variables
+
+You can also set the logging level and format using environment variables:
+
+- `LOG_LEVEL`: Set the logging level.
+- `LOG_STRUCTURED`: Enable structured logging.
+
+```bash
+LOG_LEVEL=DEBUG LOG_STRUCTURED=1 python my_app.py
+```
+
+### Structured Logging in Production
+
+To enable structured logging (JSON logs), you can either set the `--log-structured` flag when running your application or configure it programmatically using `setup()`:
+
+```bash
+python my_app.py --log-level DEBUG --log-structured
+```
+
+In structured logging mode, each log entry is a JSON object with the following fields:
+
+- `level`: The log level (e.g., "info", "error").
+- `msg`: The log message.
+- `source`: The file and line number where the log occurred.
+- `time`: The timestamp of the log event.
+- `thread`: The thread ID in a shortened format.
+- `name`: The logger name.
+
+Example structured log output:
+
+```json
+{
+  "level": "info",
+  "msg": "Application started",
+  "source": "app.py:42",
+  "time": "2023-09-23T14:22:35.000+00:00",
+  "thread": "f47c",
+  "name": "my_app"
+}
+```
+
+### Custom Context with `set_context()`
+
+In structured logging mode, you can attach additional context to each log message by calling `set_context()`. This context is logged alongside the usual fields, allowing you to track custom metadata.
+
+```python
+from my_logging_module import set_context
+
+# Set custom context
+set_context({"user_id": "12345", "transaction_id": "abcde"})
+
+# The custom context will now appear in each structured log message
+```
+
+### Handling Large Log Messages with `log_multipart()`
+
+When logging large messages (e.g., serialized data or files), the `log_multipart()` function compresses and splits the message into smaller chunks to prevent issues with log size limits.
+
+```python
+from my_logging_module import log_multipart
+
+# Log a large message
+log_multipart(logging.getLogger(), b"Large data to be logged")
+```
+
+This function will automatically split the message and log each chunk, ensuring the entire message is captured.
+
+---
+
+## Customizing the Logging Format
+
+### Human-Readable Logs
+
+By default, when not using structured logging, logs are output in a colored format, with color-coding based on the log level:
+
+- **DEBUG**: Gray
+- **INFO**: Cyan
+- **WARNING**: Yellow
+- **ERROR/CRITICAL**: Red
+
+You can further customize the format by modifying the `AwesomeFormatter` class, which is used for formatting logs in human-readable mode. It also shortens thread IDs for easier readability.
+
+### Enforcing Logging Standards
+
+To enforce standards in your logging messages, such as preventing trailing dots in log messages, the module provides the `check_trailing_dot()` decorator. This can be applied to logging functions to raise an error if a message ends with a dot:
+
+```python
+from my_logging_module import check_trailing_dot
+
+@check_trailing_dot
+def log_message(record):
+    # Your custom logging logic
+    pass
+```
+
+---
+
+## Best Practices
+
+- Use **human-readable logs** in development for easier debugging.
+- Switch to **structured logging** in production to enable easier parsing and aggregation by log management tools.
+- **Set custom contexts** to include additional metadata in your logs, such as user IDs or request IDs, to improve traceability in production.
+- **Use multipart logging** to handle large log messages that might otherwise exceed log size limits.
+
+---
+
+## Example
+
+Here's a full example of how to use structured logging with command-line configuration:
+
+```python
+import argparse
+import logging
+from flogging import add_logging_args, set_context, setup
+
+# Initialize logging
+setup(level="INFO", structured=False)  # Human-readable format
+# Create argument parser
+parser = argparse.ArgumentParser(description="My Application")
+add_logging_args(parser)
+
+# Parse arguments and setup logging
+args = parser.parse_args()
+
+# Set additional context for structured logging
+set_context({"request_id": "123abc"})
+
+# Start logging messages
+logger = logging.getLogger("my_app")
+logger.info("Application started")
+```
+
diff --git a/flogging/__init__.py b/flogging/__init__.py
index 3198ead..51d0c1a 100644
--- a/flogging/__init__.py
+++ b/flogging/__init__.py
@@ -1 +1 @@
-from flogging.flogging import setup
+from flogging.flogging import add_logging_args, setup, set_context