Fix Python terminology (#198)

apps/opik-documentation/documentation/docs/tracing/log_distributed_traces.md

@@ -5,7 +5,7 @@ sidebar_label: Log Distributed Traces
 
 # Log Distributed Traces
 
-When working with complex LLM applications, it is common to need to track a traces across multiple services. Comet supports distributed tracing out of the box when integrating using function annotators using a mechanism that is similar to how OpenTelemetry implements distributed tracing.
+When working with complex LLM applications, it is common to need to track a traces across multiple services. Comet supports distributed tracing out of the box when integrating using function decorators using a mechanism that is similar to how OpenTelemetry implements distributed tracing.
 
 For the purposes of this guide, we will assume that you have a simple LLM application that is made up of two services: a client and a server. We will assume that the client will create the trace and span, while the server will add a nested span. In order to do this, the `trace_id` and `span_id` will be passed in the headers of the request from the client to the server.
 

apps/opik-documentation/documentation/docs/tracing/log_traces.md

@@ -15,15 +15,15 @@ To log traces to the Comet LLM Evaluation platform using the Python SDK, you wil
 pip install opik
 ```
 
-Once the SDK is installed, you can log traces to using one our Comet's integration, function annotations or manually.
+Once the SDK is installed, you can log traces to using one our Comet's integration, function decorators or manually.
 
 :::tip
 Opik has a number of integrations for popular LLM frameworks like LangChain or OpenAI, checkout a full list of integrations in the [integrations](/tracing/integrations/overview.md) section.
 :::
 
-## Log using function annotators
+## Log using function decorators
 
-If you are manually defining your LLM chains and not using LangChain for example, you can use the `track` function annotators to track LLM calls:
+If you are manually defining your LLM chains and not using LangChain for example, you can use the `track` function decorators to track LLM calls:
 
 ```python
 from opik import track
@@ -61,7 +61,7 @@ print(result)
 ```
 
 :::tip
-    If the `track` function annotators are used in conjunction with the `track_openai` or `CometTracer` callbacks, the LLM calls will be automatically logged to the corresponding trace.
+    If the `track` function decorators are used in conjunction with the `track_openai` or `CometTracer` callbacks, the LLM calls will be automatically logged to the corresponding trace.
 :::
 
 ## Log traces and spans manually