Additions to community contributed articles (#987)

Author: atarora

docs/community/index.md

@@ -37,6 +37,11 @@ Explore insightful articles, tutorials, and stories written by and for our commu
     - [RAG Evaluation: Necessity and Challenge](https://tech.beatrust.com/entry/2024/05/02/RAG_Evaluation%3A_Necessity_and_Challenge)
     - [RAG Evaluation : Computational Metrics in RAG and Calculation Methods in Ragas](https://tech.beatrust.com/entry/2024/05/02/RAG_Evaluation_%3A_Computational_Metrics_in_RAG_and_Calculation_Methods_in_Ragas)
     - [RAG Evaluation: Assessing the Usefulness of Ragas](https://tech.beatrust.com/entry/2024/05/02/RAG_Evaluation%3A_Assessing_the_Usefulness_of_Ragas)
+- [Atita Arora](https://www.linkedin.com/in/atitaarora/) writes about [Evaluating Retrieval Augmented Generation using RAGAS](https://superlinked.com/vectorhub/articles/retrieval-augmented-generation-eval-qdrant-ragas), an end-to-end tutorial on building RAG using [Qdrant](https://qdrant.tech/) and [Langchain](https://www.langchain.com/) and evaluating it with RAGAS. 
+  - *Bonus content* : Learn how to create an evaluation dataset that serves as a reference point for evaluating our RAG pipeline, Understand the RAGAS evaluation metrics and how to make sense of them and putting them in action to test a Naive RAG pipeline and measure its performance using RAGAS metrics. 
+  - *Code walkthrough* : https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-ragas
+  - *Code walkthrough using [Deepset Haystack](https://haystack.deepset.ai/) and [Mixedbread.ai](https://www.mixedbread.ai/)* : https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-ragas-haystack
+
 
 ## **📅 Events**
 

docs/concepts/metrics/answer_relevance.md

@@ -2,7 +2,7 @@
 
 The evaluation metric, Answer Relevancy, focuses on assessing how pertinent the generated answer is to the given prompt. A lower score is assigned to answers that are incomplete or contain redundant information and higher scores indicate better relevancy. This metric is computed using the `question`, the `context` and the `answer`. 
 
-The Answer Relevancy is defined as the mean cosine similartiy of the original `question` to a number of artifical questions, which where generated (reverse engineered) based on the `answer`: 
+The Answer Relevancy is defined as the mean cosine similarity of the original `question` to a number of artifical questions, which where generated (reverse engineered) based on the `answer`: 
 
 ```{math}
 \text{answer relevancy} = \frac{1}{N} \sum_{i=1}^{N} cos(E_{g_i}, E_o)