diff --git a/docs/source/developers/index.md b/docs/source/developers/index.md
index 6a91c6758..644dc0a4e 100644
--- a/docs/source/developers/index.md
+++ b/docs/source/developers/index.md
@@ -392,42 +392,6 @@ custom = "custom_package:CustomChatHandler"
 Then, install your package so that Jupyter AI adds custom chat handlers
 to the existing chat handlers.
 
-## Streaming output
-
-Jupyter AI supports streaming output in the chat session. When submitting a chat prompt, the response is streamed so that the time to first token seen is minimal, leading to a pleasing user experience. Streaming the response is also visually pleasing. Streaming output is also enabled when the contextual command `@file` is used, where standard chat is enhanced with supplying a file as context using `@file`. Support for streaming responses in standard chat and contextual chat is provided by the base chat handler in `base.py` through the `BaseChatHandler` class with functions `_start_stream`, `_send_stream_chunk`, and `stream_reply`.
-
-The streaming functionality uses LangChain's Expression Language (LCEL). LCEL is a declarative way to compose [Runnables](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) into chains. Any chain constructed this way will automatically have sync, async, batch, and streaming support. The main composition primitives are RunnableSequence and RunnableParallel. The `stream_reply` function leverages the LCEL Runnable as shown here:
-
-```python
-async def stream_reply(
-    self,
-    input: Input,
-    human_msg: HumanChatMessage,
-    config: Optional[RunnableConfig] = None,
-):
-    """
-    Streams a reply to a human message by invoking
-    `self.llm_chain.astream()`. A LangChain `Runnable` instance must be
-    bound to `self.llm_chain` before invoking this method.
-
-    Arguments
-    ---------
-    - `input`: The input to your runnable. The type of `input` depends on
-    the runnable in `self.llm_chain`, but is usually a dictionary whose keys
-    refer to input variables in your prompt template.
-
-    - `human_msg`: The `HumanChatMessage` being replied to.
-
-    - `config` (optional): A `RunnableConfig` object that specifies
-    additional configuration when streaming from the runnable.
-    """
-    assert self.llm_chain
-    assert isinstance(self.llm_chain, Runnable)
-```
-
-The rest of the function chunks up the response and streams it one chunk at a time.
-
-
 ## Custom message footer
 
 You can provide a custom message footer that will be rendered under each message