diff --git a/docs/index.md b/docs/index.md
index cd3c002..207bd93 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -8,11 +8,6 @@ nav_order: 1
A [100-line](https://github.com/zachary62/miniLLMFlow/blob/main/minillmflow/__init__.py) minimalist LLM framework for *Agents, Task Decomposition, RAG, etc*.
-
-

-
-
-## Core Abstraction
We model the LLM workflow as a **Nested Directed Graph**:
- **Nodes** handle simple (LLM) tasks.
@@ -22,7 +17,12 @@ We model the LLM workflow as a **Nested Directed Graph**:
- **Batch** Nodes/Flows for data-intensive tasks.
- **Async** Nodes/Flows allow waits or **Parallel** execution
-To learn more:
+
+

+
+
+## Core Abstraction
+
- [Node](./node.md)
- [Flow](./flow.md)
- [Communication](./communication.md)
@@ -30,22 +30,12 @@ To learn more:
- [(Advanced) Async](./async.md)
- [(Advanced) Parallel](./parallel.md)
-## LLM Wrapper & Tools
+## Low-Level Details (We Do Not Provide)
-**We DO NOT provide built-in LLM wrappers and tools!**
-
-I believe it is a *bad practice* to provide low-level implementations in a general framework:
-- **APIs change frequently.** Hardcoding them makes maintenance a nightmare.
-- You may need **flexibility.** E.g., using fine-tunined LLMs or deploying local ones.
-- You may need **optimizations.** E.g., prompt caching, request batching, response streaming...
-
-We provide some simple example implementations:
- [LLM Wrapper](./llm.md)
- [Tool](./tool.md)
-## Paradigm
-
-Based on the core abstraction, we implement common high-level paradigms:
+## High-Level Paradigm
- [Structured Output](./structure.md)
- Task Decomposition
diff --git a/docs/llm.md b/docs/llm.md
index 55e44ab..789cd06 100644
--- a/docs/llm.md
+++ b/docs/llm.md
@@ -62,3 +62,8 @@ def call_llm(prompt):
return response
```
+## Why Not Provide Built-in LLM Wrappers?
+I believe it is a **bad practice** to provide LLM-specific implementations in a general framework:
+- **LLM APIs change frequently**. Hardcoding them makes maintenance a nighmare.
+- You may need **flexibility** to switch vendors, use fine-tuned models, or deploy local LLMs.
+- You may need **optimizations** like prompt caching, request batching, or response streaming.