diff --git a/docs/evaluation/how_to_guides/unit_testing.mdx b/docs/evaluation/how_to_guides/unit_testing.mdx
index 54a31549..6e75a960 100644
--- a/docs/evaluation/how_to_guides/unit_testing.mdx
+++ b/docs/evaluation/how_to_guides/unit_testing.mdx
@@ -4,12 +4,17 @@ sidebar_position: 7
 
 # How to unit test applications (Python only)
 
-LangSmith functional tests are assertions and expectations designed to **quickly** identify obvious bugs and regressions in your AI system. Relative to evaluations, tests typically are designed to be **fast** and **cheap** to run, focusing on **specific** functionality and edge cases.
+LangSmith functional tests are assertions and expectations designed to **quickly** identify obvious bugs and regressions in your AI system. 
+Relative to evaluations, tests typically are designed to be **fast** and **cheap** to run, focusing on **specific** functionality and edge cases with binary assertions.
 We recommend using LangSmith to track any unit tests, end-to-end integration tests, or other specific assertions that touch an LLM or other non-deterministic part of your AI system.
-These should run on every commit in your CI pipeline to catch regressions early.
+Ideally these run on every commit in your CI pipeline to catch regressions early.
 
-:::note
-`@unit` currently requires `langsmith` python version `>=0.1.74` (named `@unit` for versions `>=0.1.42`). If you are interested in unit testing functionality in TypeScript or other languages, please let us know at [support@langchain.dev](mailto:support@langchain.dev).  
+:::info Version requirement
+`@unit` requires `langsmith` Python version `>=0.1.74`. 
+:::
+
+:::info TypeScript support
+If you are interested in unit testing functionality in TypeScript or other languages, please upvote/comment on [this GitHub Issue](https://github.com/langchain-ai/langsmith-sdk/issues/1321). 
 :::
 
 ## Write a @unit
@@ -17,8 +22,8 @@ These should run on every commit in your CI pipeline to catch regressions early.
 To write a LangSmith functional test, decorate your test function with `@unit`.
 If you want to track the full nested trace of the system or component being tested, you can mark those functions with `@traceable`. For example:
 
-```python
- my_app/main.py
+```python 
+# my_app/main.py
 from langsmith import traceable
 
 @traceable # Optional
@@ -30,8 +35,8 @@ def generate_sql(user_query):
 
 Then define your test:
 
-```python tests/test_my_app.py
- tests/test_my_app.py
+```python
+# tests/test_my_app.py
 from langsmith import unit
 from my_app.main import generate_sql