diff --git a/README.md b/README.md
index 799482e..976fbe3 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@
 
 **Laygo** is the lightweight Python library for data pipelines that I wish existed when I first started. It's designed from the ground up to make data engineering simpler, cleaner, and more intuitive, letting you build resilient, in-memory data workflows with an elegant, fluent API.
 
-It's built to grow with you. Scale seamlessly from a single local script to thousands of concurrent serverless functions with minimal operational overhead. 
+It's built to grow with you. Scale seamlessly from a single local script to thousands of concurrent serverless functions with minimal operational overhead.
 
 **Key Features:**
 
@@ -31,6 +31,8 @@ It's built to grow with you. Scale seamlessly from a single local script to thou
 
 - **Effortless Parallelism**: Accelerate CPU-intensive tasks seamlessly.
 
+- **Fan-out Processing**: Split pipelines into multiple concurrent branches for parallel analysis of the same dataset.
+
 - **Distributed by Design**: Your pipeline script is both the manager and the worker. When deployed as a serverless function or a container, this design allows you to scale out massively by simply running more instances of the same code. Your logic scales the same way on a thousand cores as it does on one.
 
 - **Powerful Context Management**: Share state and configuration across your entire pipeline for advanced, stateful processing.
@@ -197,6 +199,110 @@ results = (
 )
 ```
 
+### Pipeline Branching (Fan-out Processing)
+
+```python
+from laygo import Pipeline
+from laygo.transformers.transformer import createTransformer
+
+# Sample data: customer orders
+orders = [
+    {"id": 1, "customer": "Alice", "amount": 150, "product": "laptop"},
+    {"id": 2, "customer": "Bob", "amount": 25, "product": "book"},
+    {"id": 3, "customer": "Charlie", "amount": 75, "product": "headphones"},
+    {"id": 4, "customer": "Diana", "amount": 200, "product": "monitor"},
+    {"id": 5, "customer": "Eve", "amount": 30, "product": "mouse"},
+]
+
+# Create different analysis branches
+high_value_analysis = (
+    createTransformer(dict)
+    .filter(lambda order: order["amount"] > 100)
+    .map(lambda order: {
+        "customer": order["customer"],
+        "amount": order["amount"],
+        "category": "high_value"
+    })
+)
+
+product_summary = (
+    createTransformer(dict)
+    .map(lambda order: {"product": order["product"], "count": 1})
+    # Group by product and sum counts (simplified example)
+)
+
+customer_spending = (
+    createTransformer(dict)
+    .map(lambda order: {
+        "customer": order["customer"],
+        "total_spent": order["amount"]
+    })
+)
+
+# Branch the pipeline into multiple concurrent analyses
+results = Pipeline(orders).branch({
+    "high_value_orders": high_value_analysis,
+    "products": product_summary,
+    "customer_totals": customer_spending
+})
+
+print("High value orders:", results["high_value_orders"])
+# [{'customer': 'Alice', 'amount': 150, 'category': 'high_value'}, 
+#  {'customer': 'Diana', 'amount': 200, 'category': 'high_value'}]
+
+print("Product analysis:", len(results["products"]))
+# 5 (all products processed)
+
+print("Customer spending:", len(results["customer_totals"]))  
+# 5 (all customers processed)
+```
+
+### Advanced Branching with Error Isolation
+
+```python
+from laygo import Pipeline
+from laygo.transformers.transformer import createTransformer
+
+# Data with potential issues
+mixed_data = [1, 2, "invalid", 4, 5, None, 7, 8]
+
+# Branch 1: Safe numeric processing
+safe_numbers = (
+    createTransformer(int | str | None)
+    .filter(lambda x: isinstance(x, int) and x is not None)
+    .map(lambda x: x * 2)
+)
+
+# Branch 2: String processing with error handling
+string_processing = (
+    createTransformer(int | str | None)
+    .filter(lambda x: isinstance(x, str))
+    .map(lambda x: f"processed_{x}")
+    .catch(lambda t: t.map(lambda x: "error_handled"))
+)
+
+# Branch 3: Statistical analysis
+stats_analysis = (
+    createTransformer(int | str | None)
+    .filter(lambda x: isinstance(x, int) and x is not None)
+    .map(lambda x: x)  # Pass through for stats
+)
+
+# Execute all branches concurrently
+results = Pipeline(mixed_data).branch({
+    "numbers": safe_numbers,
+    "strings": string_processing,
+    "stats": stats_analysis
+}, batch_size=100)
+
+print("Processed numbers:", results["numbers"])  # [2, 4, 8, 10, 14, 16]
+print("Processed strings:", results["strings"])  # ['processed_invalid']
+print("Stats data:", results["stats"])           # [1, 2, 4, 5, 7, 8]
+
+# Each branch processes the complete dataset independently
+# Errors in one branch don't affect others
+```
+
 ### Error Handling and Recovery
 
 ```python
diff --git a/laygo/pipeline.py b/laygo/pipeline.py
index a637085..c94dfd1 100644
--- a/laygo/pipeline.py
+++ b/laygo/pipeline.py
@@ -27,6 +27,21 @@ class Pipeline[T]:
   A Pipeline provides a high-level interface for data processing by chaining
   transformers together. It automatically manages a multiprocessing-safe
   shared context that can be accessed by all transformers in the chain.
+
+  The Pipeline supports both streaming and batch processing patterns, with
+  built-in support for buffering, branching (fan-out), and parallel processing.
+
+  Example:
+      >>> data = [1, 2, 3, 4, 5]
+      >>> result = (Pipeline(data)
+      ...           .transform(lambda t: t.filter(lambda x: x % 2 == 0))
+      ...           .transform(lambda t: t.map(lambda x: x * 2))
+      ...           .to_list())
+      >>> result  # [4, 8]
+
+  Note:
+      Most pipeline operations consume the internal iterator, making the
+      pipeline effectively single-use unless the data source is re-initialized.
   """
 
   def __init__(self, *data: Iterable[T]) -> None:
@@ -64,14 +79,22 @@ def __del__(self) -> None:
   def context(self, ctx: PipelineContext) -> "Pipeline[T]":
     """Update the pipeline context and store a reference to the original context.
 
-    When the pipeline finishes processing, the original context will be updated
-    with the final pipeline context data.
+    The provided context will be used during pipeline execution and any
+    modifications made by transformers will be synchronized back to the
+    original context when the pipeline finishes processing.
 
     Args:
-        ctx: The pipeline context to use for this pipeline execution.
+        ctx: The pipeline context dictionary to use for this pipeline execution.
+             This should be a mutable dictionary-like object that transformers
+             can use to share state and communicate.
 
     Returns:
         The pipeline instance for method chaining.
+
+    Note:
+        Changes made to the context during pipeline execution will be
+        automatically synchronized back to the original context object
+        when the pipeline is destroyed or processing completes.
     """
     # Store reference to the original context
     self._original_context_ref = ctx
@@ -96,13 +119,21 @@ def transform[U](self, t: Callable[[Transformer[T, T]], Transformer[T, U]]) -> "
     """Apply a transformation using a lambda function.
 
     Creates a Transformer under the hood and applies it to the pipeline.
-    This is a shorthand method for simple transformations.
+    This is a shorthand method for simple transformations that allows
+    chaining transformer operations in a functional style.
 
     Args:
         t: A callable that takes a transformer and returns a transformed transformer.
+           Typically used with lambda expressions like:
+           `lambda t: t.map(func).filter(predicate)`
 
     Returns:
-        A new Pipeline with the transformed data.
+        A new Pipeline with the transformed data type.
+
+    Example:
+        >>> pipeline = Pipeline([1, 2, 3, 4, 5])
+        >>> result = pipeline.transform(lambda t: t.filter(lambda x: x % 2 == 0).map(lambda x: x * 2))
+        >>> result.to_list()  # [4, 8]
     """
     # Create a new transformer and apply the transformation function
     transformer = t(Transformer[T, T]())
@@ -125,17 +156,28 @@ def apply[U](
   ) -> "Pipeline[U]":
     """Apply a transformer to the current data source.
 
-    The pipeline's managed context is passed down to the transformer.
+    This method accepts various types of transformers and applies them to
+    the pipeline data. The pipeline's managed context is automatically
+    passed to context-aware transformers.
 
     Args:
-        transformer: Either a Transformer instance or a callable function
-                    that processes the data.
+        transformer: One of the following:
+                    - A Transformer instance (preferred for complex operations)
+                    - A callable function that takes an iterable and returns an iterator
+                    - A context-aware callable that takes an iterable and context
 
     Returns:
-        A new Pipeline with the transformed data.
+        The same Pipeline instance with transformed data (for method chaining).
 
     Raises:
         TypeError: If the transformer is not a supported type.
+
+    Example:
+        >>> pipeline = Pipeline([1, 2, 3])
+        >>> # Using a Transformer instance
+        >>> pipeline.apply(createTransformer(int).map(lambda x: x * 2))
+        >>> # Using a simple function
+        >>> pipeline.apply(lambda data: (x * 2 for x in data))
     """
     match transformer:
       case Transformer():
@@ -157,7 +199,34 @@ def branch(
     max_batch_buffer: int = 1,
     use_queue_chunks: bool = True,
   ) -> dict[str, list[Any]]:
-    """Forks the pipeline into multiple branches for concurrent, parallel processing."""
+    """Forks the pipeline into multiple branches for concurrent, parallel processing.
+
+    This is a **terminal operation** that implements a fan-out pattern where
+    the entire dataset is copied to each branch for independent processing.
+    Each branch processes the complete dataset concurrently using separate
+    transformers, and results are collected and returned in a dictionary.
+
+    Args:
+        branches: A dictionary where keys are branch names (str) and values
+                  are `Transformer` instances of any subtype.
+        batch_size: The number of items to batch together when sending data
+                    to branches. Larger batches can improve throughput but
+                    use more memory. Defaults to 1000.
+        max_batch_buffer: The maximum number of batches to buffer for each
+                          branch queue. Controls memory usage and creates
+                          backpressure. Defaults to 1.
+        use_queue_chunks: Whether to use passthrough chunking for the
+                          transformers. When True, batches are processed
+                          as chunks. Defaults to True.
+
+    Returns:
+        A dictionary where keys are the branch names and values are lists
+        of all items processed by that branch's transformer.
+
+    Note:
+        This operation consumes the pipeline's iterator, making subsequent
+        operations on the same pipeline return empty results.
+    """
     if not branches:
       self.consume()
       return {}
@@ -258,24 +327,47 @@ def _producer() -> None:
   def __iter__(self) -> Iterator[T]:
     """Allow the pipeline to be iterated over.
 
+    This makes the Pipeline compatible with Python's iterator protocol,
+    allowing it to be used in for loops, list comprehensions, and other
+    contexts that expect an iterable.
+
     Returns:
         An iterator over the processed data.
+
+    Note:
+        This operation consumes the pipeline's iterator, making subsequent
+        operations on the same pipeline return empty results.
     """
     yield from self.processed_data
 
   def to_list(self) -> list[T]:
     """Execute the pipeline and return the results as a list.
 
+    This is a terminal operation that consumes the pipeline's iterator
+    and materializes all results into memory.
+
     Returns:
         A list containing all processed items from the pipeline.
+
+    Note:
+        This operation consumes the pipeline's iterator, making subsequent
+        operations on the same pipeline return empty results.
     """
     return list(self.processed_data)
 
   def each(self, function: PipelineFunction[T]) -> None:
     """Apply a function to each element (terminal operation).
 
+    This is a terminal operation that processes each element for side effects
+    and consumes the pipeline's iterator without returning results.
+
     Args:
-        function: The function to apply to each element.
+        function: The function to apply to each element. Should be used for
+                  side effects like logging, updating external state, etc.
+
+    Note:
+        This operation consumes the pipeline's iterator, making subsequent
+        operations on the same pipeline return empty results.
     """
     for item in self.processed_data:
       function(item)
@@ -283,23 +375,36 @@ def each(self, function: PipelineFunction[T]) -> None:
   def first(self, n: int = 1) -> list[T]:
     """Get the first n elements of the pipeline (terminal operation).
 
+    This is a terminal operation that consumes up to n elements from the
+    pipeline's iterator and returns them as a list.
+
     Args:
-        n: The number of elements to retrieve.
+        n: The number of elements to retrieve. Must be at least 1.
 
     Returns:
-        A list containing the first n elements.
+        A list containing the first n elements, or fewer if the pipeline
+        contains fewer than n elements.
 
     Raises:
         AssertionError: If n is less than 1.
+
+    Note:
+        This operation partially consumes the pipeline's iterator. Subsequent
+        operations will continue from where this operation left off.
     """
     assert n >= 1, "n must be at least 1"
     return list(itertools.islice(self.processed_data, n))
 
   def consume(self) -> None:
-    """Consume the pipeline without returning results.
+    """Consume the pipeline without returning results (terminal operation).
+
+    This is a terminal operation that processes all elements in the pipeline
+    for their side effects without materializing any results. Useful when
+    the pipeline operations have side effects and you don't need the results.
 
-    This is useful when you want to execute the pipeline for side effects
-    without collecting the results.
+    Note:
+        This operation consumes the pipeline's iterator, making subsequent
+        operations on the same pipeline return empty results.
     """
     for _ in self.processed_data:
       pass
diff --git a/wiki/Home.md b/wiki/Home.md
index d86b8cb..7738101 100644
--- a/wiki/Home.md
+++ b/wiki/Home.md
@@ -19,7 +19,7 @@
 
 **Laygo** is the lightweight Python library for data pipelines that I wish existed when I first started. It's designed from the ground up to make data engineering simpler, cleaner, and more intuitive, letting you build resilient, in-memory data workflows with an elegant, fluent API.
 
-It's built to grow with you. Scale seamlessly from a single local script to thousands of concurrent serverless functions with minimal operational overhead. 
+It's built to grow with you. Scale seamlessly from a single local script to thousands of concurrent serverless functions with minimal operational overhead.
 
 **Key Features:**
 
@@ -31,6 +31,8 @@ It's built to grow with you. Scale seamlessly from a single local script to thou
 
 - **Effortless Parallelism**: Accelerate CPU-intensive tasks seamlessly.
 
+- **Fan-out Processing**: Split pipelines into multiple concurrent branches for parallel analysis of the same dataset.
+
 - **Distributed by Design**: Your pipeline script is both the manager and the worker. When deployed as a serverless function or a container, this design allows you to scale out massively by simply running more instances of the same code. Your logic scales the same way on a thousand cores as it does on one.
 
 - **Powerful Context Management**: Share state and configuration across your entire pipeline for advanced, stateful processing.
@@ -197,6 +199,110 @@ results = (
 )
 ```
 
+### Pipeline Branching (Fan-out Processing)
+
+```python
+from laygo import Pipeline
+from laygo.transformers.transformer import createTransformer
+
+# Sample data: customer orders
+orders = [
+    {"id": 1, "customer": "Alice", "amount": 150, "product": "laptop"},
+    {"id": 2, "customer": "Bob", "amount": 25, "product": "book"},
+    {"id": 3, "customer": "Charlie", "amount": 75, "product": "headphones"},
+    {"id": 4, "customer": "Diana", "amount": 200, "product": "monitor"},
+    {"id": 5, "customer": "Eve", "amount": 30, "product": "mouse"},
+]
+
+# Create different analysis branches
+high_value_analysis = (
+    createTransformer(dict)
+    .filter(lambda order: order["amount"] > 100)
+    .map(lambda order: {
+        "customer": order["customer"],
+        "amount": order["amount"],
+        "category": "high_value"
+    })
+)
+
+product_summary = (
+    createTransformer(dict)
+    .map(lambda order: {"product": order["product"], "count": 1})
+    # Group by product and sum counts (simplified example)
+)
+
+customer_spending = (
+    createTransformer(dict)
+    .map(lambda order: {
+        "customer": order["customer"],
+        "total_spent": order["amount"]
+    })
+)
+
+# Branch the pipeline into multiple concurrent analyses
+results = Pipeline(orders).branch({
+    "high_value_orders": high_value_analysis,
+    "products": product_summary,
+    "customer_totals": customer_spending
+})
+
+print("High value orders:", results["high_value_orders"])
+# [{'customer': 'Alice', 'amount': 150, 'category': 'high_value'}, 
+#  {'customer': 'Diana', 'amount': 200, 'category': 'high_value'}]
+
+print("Product analysis:", len(results["products"]))
+# 5 (all products processed)
+
+print("Customer spending:", len(results["customer_totals"]))  
+# 5 (all customers processed)
+```
+
+### Advanced Branching with Error Isolation
+
+```python
+from laygo import Pipeline
+from laygo.transformers.transformer import createTransformer
+
+# Data with potential issues
+mixed_data = [1, 2, "invalid", 4, 5, None, 7, 8]
+
+# Branch 1: Safe numeric processing
+safe_numbers = (
+    createTransformer(int | str | None)
+    .filter(lambda x: isinstance(x, int) and x is not None)
+    .map(lambda x: x * 2)
+)
+
+# Branch 2: String processing with error handling
+string_processing = (
+    createTransformer(int | str | None)
+    .filter(lambda x: isinstance(x, str))
+    .map(lambda x: f"processed_{x}")
+    .catch(lambda t: t.map(lambda x: "error_handled"))
+)
+
+# Branch 3: Statistical analysis
+stats_analysis = (
+    createTransformer(int | str | None)
+    .filter(lambda x: isinstance(x, int) and x is not None)
+    .map(lambda x: x)  # Pass through for stats
+)
+
+# Execute all branches concurrently
+results = Pipeline(mixed_data).branch({
+    "numbers": safe_numbers,
+    "strings": string_processing,
+    "stats": stats_analysis
+}, batch_size=100)
+
+print("Processed numbers:", results["numbers"])  # [2, 4, 8, 10, 14, 16]
+print("Processed strings:", results["strings"])  # ['processed_invalid']
+print("Stats data:", results["stats"])           # [1, 2, 4, 5, 7, 8]
+
+# Each branch processes the complete dataset independently
+# Errors in one branch don't affect others
+```
+
 ### Error Handling and Recovery
 
 ```python