diff --git a/docs/async.md b/docs/async.md
index 29a4367..c3cc235 100644
--- a/docs/async.md
+++ b/docs/async.md
@@ -7,7 +7,7 @@ nav_order: 5
 
 # (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:
+**Async Nodes** implement `prep_async()`, `exec_async()`, `exec_fallback_async()`, and/or `post_async()`. This is useful for:
 
 1. **prep_async()**  
    - For *fetching/reading data (files, APIs, DB)* in an I/O-friendly way.
@@ -18,7 +18,6 @@ nav_order: 5
 3. **post_async()**  
    - For *awaiting user feedback*, *coordinating across multi-agents* or any additional async steps after `exec_async()`.
 
-Each step can be either sync or async; the framework automatically detects which to call.
 
 **Note**: `AsyncNode` must be wrapped in `AsyncFlow`. `AsyncFlow` can also include regular (sync) nodes.
 
@@ -59,6 +58,4 @@ async def main():
     print("Final Summary:", shared.get("summary"))
 
 asyncio.run(main())
-```
-
-Keep it simple: go async only when needed, handle errors gracefully, and leverage Python’s `asyncio`.
+```
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diff --git a/docs/parallel.md b/docs/parallel.md
index b7da09f..f822fbe 100644
--- a/docs/parallel.md
+++ b/docs/parallel.md
@@ -7,11 +7,11 @@ nav_order: 6
 
 # (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. 
+**Parallel** Nodes and Flows let you run multiple **Async** Nodes and Flows  **concurrently**—for example, summarizing multiple texts at once. This can improve performance by overlapping I/O and compute. 
 
 ## AsyncParallelBatchNode
 
-Like **AsyncBatchNode**, but uses `prep_async()`, `exec_async()`, and `post_async()` in **parallel**:
+Like **AsyncBatchNode**, but run `exec_async()` in **parallel**:
 
 ```python
 class ParallelSummaries(AsyncParallelBatchNode):
@@ -47,11 +47,8 @@ await parallel_flow.run_async(shared)
 
 ## Best Practices
 
-- **Ensure Tasks Are Independent**  
-  If each item depends on the output of a previous item, **don’t** parallelize. Parallelizing dependent tasks can lead to inconsistencies or race conditions.
+- **Ensure Tasks Are Independent**: If each item depends on the output of a previous item, **do not** parallelize.
 
-- **Beware Rate Limits**  
-  Parallel calls can **quickly** trigger rate limits on LLM services. You may need a **throttling** mechanism (e.g., semaphores or sleep intervals) to avoid hitting vendor limits.
+- **Beware of Rate Limits**: Parallel calls can **quickly** trigger rate limits on LLM services. You may need a **throttling** mechanism (e.g., semaphores or sleep intervals).
 
-- **Consider Single-Node Batch APIs**  
-  Some LLMs offer a **batch inference** API where you can send multiple prompts in a single call. This is more complex to implement but can be more efficient than launching many parallel requests. Conceptually, it can look similar to an **AsyncBatchNode** or **BatchNode**, but the underlying call bundles multiple items into **one** request.
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+- **Consider Single-Node Batch APIs**: Some LLMs offer a **batch inference** API where you can send multiple prompts in a single call. This is more complex to implement but can be more efficient than launching many parallel requests and mitigates rate limits.