Wording

docs/source/conceptual/streaming.md

@@ -2,17 +2,17 @@
 
 ## What is Streaming?
 
-With streaming, the server returns the tokens as they are being generated by the LLM. This enables showing progressive generations to the user rather than having to wait for the full generation. Streaming is an essential aspect of the end-user experience as it reduces latency, one of the most critical aspects of a smooth experience.
+With streaming, the server returns the tokens as the LLM generates them. This enables showing progressive generations to the user rather than waiting for the whole generation. Streaming is an essential aspect of the end-user experience as it reduces latency, one of the most critical aspects of a smooth experience.
 
 ![A diff of streaming vs non streaming](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/streaming-generation-visual.gif)
 
-With token streaming, the server can start returning the tokens as they are generated without having to wait for all of them to be generated. The users start to see something happening much earlier than before the work is done. This has different positive effects:
+With token streaming, the server can start returning the tokens before having to wait for the whole generation. The users start to see something happening much earlier than before the work is done. This has different positive effects:
 
-* For extremely long queries, users can get results orders of magnitude earlier.
+* Users can get results orders of magnitude earlier for extremely long queries.
 * Seeing something in progress allows users to stop the generation if it's not going in the direction they expect.
-* Perceived latency is lower when results are shown in early stages.
+* Perceived latency is lower when results are shown in the early stages.
 
-For example, think that a system can generate 100 tokens per second. If the system generates 1000 tokens, with the non-streaming setup, users need to wait 10 seconds to get results. On the other hand, with the streaming setup, users get initial results immediately, and, although end-to-end latency will be the same, they have seen half of the generation after five seconds. We've built an interactive demo that shows non-streaming vs streaming side-by-side. Click **generate** below.
+For example, think that a system can generate 100 tokens per second. If the system generates 1000 tokens, with the non-streaming setup, users need to wait 10 seconds to get results. On the other hand, with the streaming setup, users get initial results immediately, and although end-to-end latency will be the same, they have seen half of the generation after five seconds. We've built an interactive demo that shows non-streaming vs streaming side-by-side. Click **generate** below.
 
 <div class="block dark:hidden">
 	<iframe 
@@ -91,10 +91,12 @@ curl -N 127.0.0.1:8080/generate_stream \
 
 ## How does Streaming work under the hood?
 
-Under the hood, TGI uses Server-Sent Events (SSE). In a SSE Setup, a client sends a request with the data, opening a HTTP connection and subscribing to updates. Afterwards, the server sends data to the client. There is no need for further requests, the server will keep sending the data. SSEs are unidirectional, meaning that the client does not send further requests to the server. SSE sends data over HTTP, making it easy to 
+Under the hood, TGI uses Server-Sent Events (SSE). In an SSE Setup, a client sends a request with the data, opening an HTTP connection and subscribing to updates. Afterward, the server sends data to the client. There is no need for further requests; the server will keep sending the data. SSEs are unidirectional, meaning the client does not send other requests to the server. SSE sends data over HTTP, making it easy to
 
 SSEs are different than:
-* Polling: where the client keeps making calls to the server to get data. This means that the server might return empty responses and cause overhead.
-* Webhooks: where there is a bi-directional connection. That is, the server can send information to the client, but the client can also send information to the server after the first request. Webhooks are more complex to operate as they don't only use HTTP.
+* Polling: where the client keeps calling the server to get data. This means that the server might return empty responses and cause overhead.
+* Webhooks: where there is a bi-directional connection. The server can send information to the client, but the client can also send data to the server after the first request. Webhooks are more complex to operate as they don’t only use HTTP.
 
-One of the limitations of Server-Sent Events is that they limit how many concurrent requests can handle by the server. Instead of timing out when there are too many SSE connections, TGI returns a HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g. it could display a busy error to the user or it could retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing to handle backpressure.
+One of the limitations of Server-Sent Events is that they limit how many concurrent requests can handle by the server. Instead of timing out when there are too many SSE connections, TGI returns a HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g. it could display a busy error to the user or it could retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing to handle backpressure.
+
+One of the limitations of Server-Sent Events is that they limit how many concurrent requests can handle by the server. Instead of timing out when there are too many SSE connections, TGI returns an HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g., it could display a busy error to the user or retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing clients to handle backpressure.