update video

qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md

@@ -37,6 +37,8 @@ Alongside Qdrant we will use Quotient, which provides a seamless way to evaluate
 
 [Quotient](https://www.quotientai.co/) is a platform that provides tooling for AI developers to build evaluation frameworks and conduct experiments on their products. Evaluation is how teams surface the shortcomings of their applications and improve performance in key benchmarks such as faithfulness, and semantic similarity. Iteration is key to building innovative AI products that will deliver value to end users.
 
+> 💡 The [accompanying notebook](https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-quotient) for this exercise can be found on GitHub for future reference.
+
 ## Summary of key findings
 
 1. **Irrelevance and Hallucinations**: When the documents retrieved are irrelevant, evidenced by low scores in both Chunk Relevance and Context Relevance, the model is prone to generating inaccurate or fabricated information.
@@ -678,12 +680,10 @@ The significant variations in *Context Relevance* suggest that **certain questio
 Furthermore, there's ongoing **exploration required on the generative aspect** of RAG. 
 Modifying LLMs or prompts can substantially impact the overall quality of responses.
 
-> 💡 The [accompanying notebook](https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-quotient) can be accessed on GitHub for future reference.
-
 This iterative process demonstrates how, starting from scratch, continual evaluation and adjustments throughout experimentation can lead to the development of an enhanced RAG system.
 
 ## Watch this workshop on YouTube
 
-> A workshop version of this article is [available on YouTube](https://www.youtube.com/watch?v=3MEMPZR1aZA).
+> A workshop version of this article is [available on YouTube](https://www.youtube.com/watch?v=3MEMPZR1aZA). Follow along using our [GitHub notebook](https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-quotient).
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/3MEMPZR1aZA?si=n38oTBMtH3LNCTzd" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>