diff --git a/docs/framework/ai-module.mdx b/docs/framework/ai-module.mdx
index 4754f5725..aab86f566 100644
--- a/docs/framework/ai-module.mdx
+++ b/docs/framework/ai-module.mdx
@@ -138,7 +138,7 @@ graph.add_file(file)
 
 # Remove the file from the graph
 graph.remove_file(file)
-````
+```
 
 #### Applying Graphs to Conversation completion
 
@@ -228,10 +228,10 @@ response = conversation.complete(tools=tool, max_tool_depth=7)
 You can pass either a single tool or a list of tools to the `complete()` or `stream_complete()` methods. The tools can be a combination of FunctionTool, Graph, or JSON-defined tools.
 
 ```python
-from writer.ai import FunctionTool, retrieve_graph
+from writer.ai import create_function_tool, retrieve_graph
 
 # Define a function tool
-tool1 = FunctionTool(
+tool1 = create_function_tool(
     name="get_data",
     callable=lambda x: f"Data for {x}",
     parameters={"x": {"type": "string", "description": "Input value"}}
@@ -245,7 +245,9 @@ response = conversation.complete(tools=[tool1, graph])
 ```
 
 ## Text generation without a conversation state
-These `complete` and `stream_complete` methods are designed for one-off text generation without the need to manage a conversation state. They return the model's response as a string. Each function accepts a `config` dictionary allowing call-specific configurations.
+
+### Text generation against a string prompt
+`complete` and `stream_complete` methods are designed for one-off text generation without the need to manage a conversation state. They return the model's response as a string. Each function accepts a `config` dictionary allowing call-specific configurations.
 
 <CodeGroup>
 ```python complete
@@ -260,4 +262,81 @@ for text_chunk in stream_complete("Explore the benefits of AI.", config={'temper
 ```
 </CodeGroup>
 
+### Text generation against graphs
+The `ask` and `stream_ask` methods allow you to query one or more graphs to generate responses from the information stored within them.
+
+#### Two approaches to questioning graphs
+
+There are two ways to query graphs, depending on your needs:
+1.	**Graph-Level Methods** (`Graph.ask`, `Graph.stream_ask`): Used when working with a single graph instance. These methods are tied directly to the Graph object, encapsulating operations within that instance.
+2.	**Module-Level Methods** (`writer.ai.ask`, `writer.ai.stream_ask`): Designed for querying multiple graphs simultaneously. These methods operate on a broader scale, allowing mixed inputs of graph objects and IDs.
+
+<Note>
+•	Use graph-level methods when working with a single graph instance.
+•	Use module-level methods when querying multiple graphs or when graph IDs are your primary input.
+</Note>
+
+#### Parameters
+
+Both methods include:
+	• `question: str`: The main query for the LLM.
+	• *Optional* `subqueries: bool` (default: `False`): Allows the LLM to generate additional questions during response preparation for more detailed answers. Enabling this might increase response time.
+
+Method-level methods require:
+    • `graphs_or_graph_ids: list[Graph | str]`: A list of graphs to use for the question. You can pass `Graph` objects directly into the list, use graph IDs in string form, or a mix of both.
+
+#### Graph-level methods
+
+The graph-level methods, `Graph.ask` and `Graph.stream_ask`, are designed for interacting with a single graph. By calling these methods on a specific `Graph` instance, you can easily pose questions and retrieve answers tailored to that graph’s content.
+
+<CodeGroup>
+```python ask
+# Retrieve a specific graph
+graph = retrieve_graph("f47ac10b-58cc-4372-a567-0e02b2c3d479")
+
+# Pose a question to the graph and get a complete response
+response = graph.ask("What are the benefits of renewable energy?")
+print(response)
+```
+```python stream_ask
+# Retrieve a specific graph
+graph = retrieve_graph("f47ac10b-58cc-4372-a567-0e02b2c3d479")
+
+# Pose a question and stream the response in chunks
+for chunk in graph.stream_ask("Explain the history of solar energy."):
+    print(chunk)
+```
+</CodeGroup>
+
+#### Module-level methods
+The module-level methods, `writer.ai.ask` and `writer.ai.stream_ask`, are designed for querying multiple graphs simultaneously. They are useful when you need to aggregate or compare data across multiple graphs.
+
+<CodeGroup>
+```python ask
+from writer.ai import ask
+
+# Pose a question to multiple graphs
+response = ask(
+    question="What are the latest advancements in AI?",
+    graphs_or_graph_ids=[
+        "550e8400-e29b-41d4-a716-446655440000",
+        "123e4567-e89b-12d3-a456-426614174000"
+        ]
+)
+print(response)
+```
+```python stream_ask
+from writer.ai import stream_ask
+
+# Stream responses from multiple graphs
+for chunk in stream_ask(
+    question="Describe the key features of renewable energy sources.",
+    graphs_or_graph_ids=[
+        "550e8400-e29b-41d4-a716-446655440000",
+        "123e4567-e89b-12d3-a456-426614174000"
+        ]
+):
+    print(chunk)
+```
+</CodeGroup>