Merge pull request #471 from superlinked/revert-470-update-rag-app-co…

…mm-sys-article

Revert "Update rag-application-communication-system.md"

docs/articles/rag-application-communication-system.md

@@ -96,10 +96,10 @@ In practical terms:
 In this time of LLMs, older information retrieval methods and indicators continue to hold a lot of unrealized value, especially now that it's possible to generate/extract many key data features at scale. Jo Kristian Bergum from Vespa, for example, has [convincingly demonstrated](https://blog.vespa.ai/improving-retrieval-with-llm-as-a-judge/) how classic info retrieval evaluation design and metrics (precision at k, recall) can be effectively repurposed using emerging practices in AI, such as LLM-as-a-Judge - grounded on a small but scalable relevant dataset. Intensive data work that would have been available only to large scale organizations is now scalable with far fewer resources.
 
 >  
->**GOING HYBRID**            
->    - *Indexation*:  traditional keyword matching + modern embedding-based similarity
->    - *Searching*:  keyword-based search + vector search
->    - *Evaluation*:  precision at k, recall + LLM-as-a-judge
+>**GOING HYBRID**
+>- *Indexation*:  traditional keyword matching + modern embedding-based similarity
+>- *Searching*:  keyword-based search + vector search
+>- *Evaluation*:  precision at k, recall + LLM-as-a-judge
 >  
 
 Generative AI within a RAG communication system shouldn't be looking to replace the classic approaches of retrieval evaluation; it should instead reshape their logistics to take full advantage of them.
@@ -112,8 +112,7 @@ A proper RAG communication system should treat data no longer as a passive refer
 2. constantly circulated across different flows
 
 >  
-> A good RAG comm system includes:            
->     bad data + classifiers + synthetic data curation
+> A good RAG comm system includes: bad data + classifiers + synthetic data curation
 >  
 
 ### 3.1 You need bad data