Add stepkurniawan community contribution

docs/community/index.md

@@ -28,9 +28,10 @@ Explore insightful articles, tutorials, and stories written by and for our commu
 - [Aishwarya Prabhat](https://www.linkedin.com/in/aishwaryaprabhat/) shares her expertise on advanced RAG techniques in her comprehensive guide, ["Performing, Evaluating & Tracking Advanced RAG (ft. AzureML, LlamaIndex & Ragas)"](https://www.linkedin.com/pulse/performing-evaluating-tracking-advanced-rag-ft-azureml-prabhat-i1rkc/).
 - Leonie (aka [@helloiamleonie](https://twitter.com/helloiamleonie?source=about_page-------------------------------------))  offers her perspective in the detailed article, ["Evaluating RAG Applications with RAGAs"](https://towardsdatascience.com/evaluating-rag-applications-with-ragas-81d67b0ee31a).
 - The joint efforts of [Erika Cardenas](https://twitter.com/ecardenas300) and [Connor Shorten](https://twitter.com/CShorten30) are showcased in their collaborative piece, ["An Overview on RAG Evaluation | Weaviate"](https://weaviate.io/blog/rag-evaluation), and their podcast with the Ragas team.
-- [Erika Cardenas](https://twitter.com/ecardenas300) further explores the "R[AG performance of hybrid search weightings (alpha)](https://www.linkedin.com/posts/erikacardenas300_i-tested-the-rag-performance-of-hybrid-search-activity-7139679925426376705-TVtc?utm_source=share&utm_medium=member_desktop)" in her recent experiment to tune weaviate alpha score using Ragas.
+- [Erika Cardenas](https://twitter.com/ecardenas300) further explores the "[RAG performance of hybrid search weightings (alpha)](https://www.linkedin.com/posts/erikacardenas300_i-tested-the-rag-performance-of-hybrid-search-activity-7139679925426376705-TVtc?utm_source=share&utm_medium=member_desktop)" in her recent experiment to tune weaviate alpha score using Ragas.
 - [Langchain’s](https://blog.langchain.dev/) work about [RAG Evaluating RAG pipelines with RAGAs and Langsmith](https://blog.langchain.dev/evaluating-rag-pipelines-with-ragas-langsmith/) provides a complete tutorial on how to leverage both tools to evaluate RAG pipelines.
 - [Plaban Nayak](https://nayakpplaban.medium.com/) shares his work [Evaluate RAG Pipeline using RAGAS](https://medium.aiplanet.com/evaluate-rag-pipeline-using-ragas-fbdd8dd466c1) on building and evaluating a simple RAG using Langchain and RAGAS
+- [Stephen Kurniawan](https://www.linkedin.com/in/stepkurniawan/) compares different RAG elements such as [Chunk Size](https://medium.com/@stepkurniawan/rag-chunk-size-experiment-e5e5ca437f44), [Vector Stores: FAISS vs ChromaDB](https://medium.com/@stepkurniawan/comparing-faiss-with-chroma-vector-stores-0953e1e619eb), [Vector Stores 2: Multiple Documents](https://medium.com/@stepkurniawan/comparing-faiss-vs-chroma-vector-store-retrieve-multiple-documents-07ad81a18851), and [Similarity Searches / Distance Metrics / Index Strategies](https://medium.com/@stepkurniawan/comparing-similarity-searches-distance-metrics-in-vector-stores-rag-model-f0b3f7532d6f). 
 
 ## **📅 Events**
 

docs/concepts/metrics/context_recall.md

@@ -19,8 +19,10 @@ Question: Where is France and what is it's capital?
 Ground truth: France is in Western Europe and its capital is Paris. 
 
 High context recall: France, in Western Europe, encompasses medieval cities, alpine villages and Mediterranean beaches. Paris, its capital, is famed for its fashion houses, classical art museums including the Louvre and monuments like the Eiffel Tower.
+    Explanation: The context answers both: (1) Where is France: Western Europe, (2) what is it's capital: Paris
 
 Low context recall: France, in Western Europe, encompasses medieval cities, alpine villages and Mediterranean beaches. The country is also renowned for its wines and sophisticated cuisine. Lascaux’s ancient cave drawings, Lyon’s Roman theater and the vast Palace of Versailles attest to its rich history.
+    Explanation: The context answers only: (1) Where is France: Western Europe, but not (2) what is it's capital: Paris
 
 ```
 

docs/concepts/metrics/context_relevancy.md

@@ -7,7 +7,7 @@ Ideally, the retrieved context should exclusively contain essential information
 
 ```{math}
 :label: context_relevancy
-\text{context relevancy} = {|S| \over |\text{Total number of sentences in retrived context}|}
+\text{context relevancy} = {|S| \over |\text{Total number of sentences in retrieved context}|}
 ```
 
 ```{hint}