add merveenoyan comments

Author: James Liounis

notebooks/en/llm_judge_evaluating_ai_search_engines_with_judges_library.ipynb

@@ -45,7 +45,7 @@
     "\n",
     "## [Setup](#setup)\n",
     "\n",
-    "We use the [Natural Questions dataset](https://paperswithcode.com/dataset/natural-questions) -- a collection of real-world Google queries and corresponding Wikipedia articles -- as our benchmark for comparing the quality of different AI search engines, as follows:\n",
+    "We use the [Natural Questions dataset](https://paperswithcode.com/dataset/natural-questions), an open-source collection of real Google queries and Wikipedia articles, to benchmark AI search engine quality.\n",
     "\n",
     "1. Start with a [**100-datapoint subset of Natural Questions**](https://huggingface.co/datasets/quotientai/natural-qa-random-100-with-AI-search-answers), which only includes human evaluated answers and their corresponding queries for correctness, clarity, and completeness. We'll use these as the ground truth answers to the queries.\n",
     "2. Use different **AI search engines** (Perplexity, Exa, and Gemini) to generate responses to the queries in the dataset.\n",
@@ -160,7 +160,7 @@
     "id": "SWYaCZEPj_WX"
    },
    "source": [
-    "You can either set the API keys from a `.env` file, such as what we are doing below, or from Google Colab secrets, in which case, uncomment the relevant code examples below."
+    "You can either set the API keys from a `.env` file, such as what we are doing below.  "
    ]
   },
   {
@@ -691,7 +691,7 @@
     "id": "mBiuYKjXiS1s"
    },
    "source": [
-    "We opt for `together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo`. Since we are using a model from [TogetherAI](https://www.together.ai), we need to set a Together API key as an environment variable."
+    "We opt for `together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo`. Since we are using a model from [TogetherAI](https://www.together.ai), we need to set a Together API key as an environment variable. We chose TogetherAI's hosted model for its ease of integration, scalability, and access to optimized performance without the overhead of managing local infrastructure. "
    ]
   },
   {