update docs

docs/async.md

@@ -1,11 +1,11 @@
 ---
 layout: default
-title: "Async"
+title: "(Advanced) Async"
 parent: "Core Abstraction"
 nav_order: 5
 ---
 
-# Async
+# (Advanced) Async
 
 **Mini LLM Flow** allows fully asynchronous nodes by implementing `prep_async()`, `exec_async()`, `exec_fallback_async()`, and/or `post_async()`. This is useful for:
 

docs/index.md

@@ -6,16 +6,15 @@ nav_order: 1
 
 # Mini LLM Flow
 
-A [100-line](https://github.com/zachary62/miniLLMFlow/blob/main/minillmflow/__init__.py) minimalist LLM framework for agents, task decomposition, RAG, etc.
+A [100-line](https://github.com/zachary62/miniLLMFlow/blob/main/minillmflow/__init__.py) minimalist LLM framework for *Agents, Task Decomposition, RAG, etc*.
 
-We model the LLM workflow as a **Nested Flow**:
-- Each **Node** handles a simple LLM task.
-- Nodes are chained together to form a **Flow** for compute-intensive tasks.
-- One Node can be chained to multiple Nodes through **Actions** as an agent.
-- A Flow can be treated as a Node for **Nested Flows**.
-- Both Nodes and Flows can be **Batched** for data-intensive tasks.
-- Nodes and Flows can be **Async** for user inputs.
-- **Async** Nodes and Flows can be executed in **Parallel**.
+We model the LLM workflow as a **Nested Directed Graph**:
+- **Nodes** handle simple (LLM) tasks.
+- Nodes connect through **Actions** (labeled edges) for *Agents*.  
+- **Flows** orchestrate a directed graph of Nodes for *Task Decomposition*.
+- A Flow can be used as a Node (for **Nesting**).
+- **Batch** Nodes/Flows for data-intensive tasks.
+- **Async** Nodes/Flows allow waits or **Parallel** execution
 
 <div align="center">
   <img src="https://github.com/zachary62/miniLLMFlow/blob/main/assets/minillmflow.jpg?raw=true" width="400"/>
@@ -27,8 +26,8 @@ We model the LLM workflow as a **Nested Flow**:
 - [Flow](./flow.md)
 - [Communication](./communication.md)
 - [Batch](./batch.md)
-- [Async](./async.md)
-- [Parallel](./parallel.md)
+- [(Advanced) Async](./async.md)
+- [(Advanced) Parallel](./parallel.md)
 
 ## Preparation
 

docs/node.md

@@ -7,7 +7,7 @@ nav_order: 1
 
 # Node
 
-A **Node** is the smallest building block of Mini LLM Flow. Each Node has three lifecycle methods:
+A **Node** is the smallest building block of Mini LLM Flow. Each Node has 3 steps:
 
 1. **`prep(shared)`**  
    - Reads and preprocesses data from the `shared` store for LLMs.
@@ -25,6 +25,7 @@ A **Node** is the smallest building block of Mini LLM Flow. Each Node has three
    - Examples: finalize outputs, trigger next steps, or log results.
    - Returns a **string** to specify the next action (`"default"` if nothing or `None` is returned).
 
+All 3 steps are optional. For example, you might only need to run the Prep without calling the LLM.
 
 ## Fault Tolerance & Retries
 

docs/parallel.md

@@ -1,11 +1,11 @@
 ---
 layout: default
-title: "Parallel"
+title: "(Advanced) Parallel"
 parent: "Core Abstraction"
 nav_order: 6
 ---
 
-# Parallel
+# (Advanced) Parallel
 
 **Parallel** Nodes and Flows let you run multiple tasks **concurrently**—for example, summarizing multiple texts at once. Unlike a regular **BatchNode**, which processes items sequentially, **AsyncParallelBatchNode** and **AsyncParallelBatchFlow** can fire off tasks in parallel. This can improve performance by overlapping I/O and compute.