remove result explanation

Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>

docs/source/llms/openai/notebooks/openai-code-helper.ipynb

@@ -472,16 +472,8 @@
     "#### Function Overview\n",
     "The `process_data` function aims to process a list by identifying unique elements and counting duplicates. However, the implementation has several inefficiencies and readability issues.\n",
     "\n",
-    "#### Review Feedback and Results\n",
-    "Upon reviewing `process_data` using our code review tool:\n",
-    "\n",
-    "1. **Logic Flaws**: The function's logic does not properly handle multiple occurrences of the same element. The `s` variable counts duplicates inaccurately, and the purpose of list `q` is unclear.\n",
-    "2. **Code Efficiency**: The function can be optimized using Python's built-in features like `set` for uniqueness and `collections.Counter` for counting.\n",
-    "3. **Readability and Maintainability**: Variable names are non-descriptive, leading to poor readability. Iteration is not idiomatic, and lack of comments further reduces maintainability.\n",
-    "4. **Best Practices**: Iterating directly over list elements is more Pythonic than using range and indices. List comprehensions are recommended for concise code.\n",
-    "\n",
     "#### Suggested Revised Code\n",
-    "The revised version of the code is more efficient, easier to understand, maintainable, and adheres to Python best practices. It utilizes `collections.Counter` to count element occurrences and `set` to determine unique elements, resulting in a more concise and readable implementation.\n"
+    "The output from GPT-4's analysis provides clear and concise feedback, precisely as the prompt instructed it to. With the MLflow integration of this application, the simplicity of using the tool is evident, allowing us to get high-quality guidance during the development process with as little as a single, simple function call. \n"
    ]
   },
   {