diff --git a/docs/guide.md b/docs/guide.md
index 1a71339..acde051 100644
--- a/docs/guide.md
+++ b/docs/guide.md
@@ -14,13 +14,13 @@ These system designs should be a collaboration between humans and AI assistants:
| Stage | Human | AI | Comment |
|:-----------------------|:----------:|:---------:|:------------------------------------------------------------------------|
-| 1. Requirements | ★★★ High | ★☆☆ Low | Humans understand the requirements and context best. |
-| 2. Utilities | ★★☆ Medium | ★★☆ Medium | The human is familiar with external APIs and integrations, and the AI assists with implementation. |
-| 3. Flow Design | ★★☆ Medium | ★★☆ Medium | The human identifies complex and ambiguous parts, and the AI helps with redesign. |
-| 4. Data Design | ★☆☆ Low | ★★★ High | The AI assists in designing the data schema based on the flow. |
-| 5. Implementation | ★☆☆ Low | ★★★ High | The human identifies complex and ambiguous parts, and the AI helps with redesign. |
-| 6. Optimization | ★★☆ Medium | ★★☆ Medium | The human reviews the code and evaluates the results, while the AI helps optimize. |
-| 7. Reliability | ★☆☆ Low | ★★★ High | The AI helps write test cases and address corner cases. |
+| 1. Requirements | ★★★ High | ★☆☆ Low | Humans understand the requirements and context. |
+| 2. Flow | ★★☆ Medium | ★★☆ Medium | Humans specify the high-level design, and the AI fills in the details. |
+| 3. Utilities | ★★☆ Medium | ★★☆ Medium | Humans provide available external APIs and integrations, and the AI helps with implementation. |
+| 4. Node | ★☆☆ Low | ★★★ High | The AI helps design the node types and data handling based on the flow. |
+| 5. Implementation | ★☆☆ Low | ★★★ High | The AI implements the flow based on the design. |
+| 6. Optimization | ★★☆ Medium | ★★☆ Medium | Humans evaluate the results, and the AI helps optimize. |
+| 7. Reliability | ★☆☆ Low | ★★★ High | The AI writes test cases and addresses corner cases. |
1. **Requirements**: Clarify the requirements for your project, and evaluate whether an AI system is a good fit. AI systems are:
- suitable for routine tasks that require common sense (e.g., filling out forms, replying to emails).
@@ -29,27 +29,31 @@ These system designs should be a collaboration between humans and AI assistants:
- > **If a human can’t solve it, an LLM can’t automate it!** Before building an LLM system, thoroughly understand the problem by manually solving example inputs to develop intuition.
{: .best-practice }
-2. **Utilities**: Think of the AI system as the brain for decision-making. It needs a body—these *external utility functions*—to interact with the real world:
-
-
- - 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)
- - Keep in mind that *LLM-based tasks* (e.g., summarizing text, analyzing sentiment) are **not** utility functions; rather, they are *core functions* internal in the AI system, and will be designed in step 3.
- - > **Start small!** Only include the most important ones to begin with!
- {: .best-practice }
-
-3. **Flow Design**: Outline how your system orchestrates steps.
- - Identify potential design patterns (e.g., Batch, Agent, RAG).
+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)).
- For each node, provide a high-level purpose description.
- Draw the Flow in mermaid diagram.
-4. **Data Design**: Plan how data will be stored and updated.
- - 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, design its access pattern:
+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:
+
+
+ - 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.
+ - > **Start small!** Only include the most important ones to begin with!
+ {: .best-practice }
+
+
+4. **Node Design**: Plan how each node will read and write data, and use utility functions.
+ - 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, design its type and data handling:
- `type`: Decide between Regular, Batch, or Async
- `prep`: How the node reads data
- `exec`: Which utility function this node uses
@@ -57,7 +61,7 @@ These system designs should be a collaboration between humans and AI assistants:
5. **Implementation**: Implement the initial nodes and flows based on the design.
- **“Keep it simple, stupid!”** Avoid complex features and full-scale type checking.
- - **FAIL FAST**! Refrain from `try` logic so you can quickly identify any weak points in the system.
+ - **FAIL FAST**! Avoid `try` logic so you can quickly identify any weak points in the system.
- Add logging throughout the code to facilitate debugging.
6. **Optimization**:
