diff --git a/docs/guide.md b/docs/guide.md
index ad39a6c..e97d27f 100644
--- a/docs/guide.md
+++ b/docs/guide.md
@@ -32,13 +32,12 @@ These system designs should be a collaboration between humans and AI assistants:
 
 2. **Flow Design**: Outline at a high level, describe how your AI system orchestrates nodes.
     - Identify applicable design patterns (e.g., [Map Reduce](./design_pattern/mapreduce.md), [Agent](./design_pattern/agent.md), [RAG](./design_pattern/rag.md)).
-    - Draw the Flow in a mermaid diagram. For example:
+    - Outline the flow and draw it in a mermaid diagram. For example:
       ```mermaid
       flowchart LR
           firstNode[First Node] --> secondNode[Second Node]
           secondNode --> thirdNode[Third Node]
       ```
-    - For each node in the flow, provide a high-level purpose description.
 
 3. **Utilities**: Based on the Flow Design, identify and implement necessary utility functions.
     - Think of your AI system as the brain. It needs a body—these *external utility functions*—to interact with the real world:
@@ -47,9 +46,8 @@ These system designs should be a collaboration between humans and AI assistants:
         - Reading inputs (e.g., retrieving Slack messages, reading emails)
         - Writing outputs (e.g., generating reports, sending emails)
         - Using external tools (e.g., calling LLMs, searching the web)
-
-    - NOTE: *LLM-based tasks* (e.g., summarizing text, analyzing sentiment) are **NOT** utility functions; rather, they are *core functions* internal in the AI system.
-    - For each utility function, implement it and write a simple test (e.g., under `if __name__ == "__main__":` ).
+        - **NOTE**: *LLM-based tasks* (e.g., summarizing text, analyzing sentiment) are **NOT** utility functions; rather, they are *core functions* internal in the AI system.
+    - For each utility function, implement it and write a simple test.
     - Document their input/output, as well as why they are necessary. For example:
       - *Name*: Embedding (`utils/get_embedding.py`)
       - *Input*: `str`
@@ -61,9 +59,8 @@ These system designs should be a collaboration between humans and AI assistants:
    - Start with the shared data design
       - For simple systems, use an in-memory dictionary.
       - For more complex systems or when persistence is required, use a database.
-      - **Remove Data Redundancy**: Don’t store the same data. Use in-memory references or foreign keys.
-   - For each node, specify its type, how it reads and writes data, and which utility function it uses. 
-   - Keep it specific but high-level without codes. For example:
+      - **Don't Repeat Yourself"**: Use in-memory references or foreign keys.
+   - For each node, describe its type, how it reads and writes data, and which utility function it uses. Keep it specific but high-level without codes. For example:
      - `type`: Regular (or Batch, or Async)
      - `prep`: Read "text" from the shared store
      - `exec`: Call the embedding utility function
diff --git a/docs/utility_function/llm.md b/docs/utility_function/llm.md
index 6439a4a..0d83fce 100644
--- a/docs/utility_function/llm.md
+++ b/docs/utility_function/llm.md
@@ -25,6 +25,7 @@ Here, we provide some minimal example implementations:
     call_llm("How are you?")
     ```
     > Store the API key in an environment variable like OPENAI_API_KEY for security.
+    {: .best-practice }
 
 2. Claude (Anthropic)
     ```python