bump link

docs/blog/posts/rag-and-beyond.md

@@ -13,7 +13,7 @@ authors:
 
 # RAG is more than just embedding search
 
-With the advent of large language models (LLM), retrival augmented generation (RAG) has become a hot topic. However throught the past year of [helping startups](https://jxnl.notion.site/Working-with-me-ec2bb36a5ac048c2a8f6bd888faea6c2?pvs=4) integrate LLMs into their stack I've noticed that the pattern of taking user queries, embedding them, and directly searching a vector store is effectively demoware.
+With the advent of large language models (LLM), retrival augmented generation (RAG) has become a hot topic. However throught the past year of [helping startups](https://jxnl.co) integrate LLMs into their stack I've noticed that the pattern of taking user queries, embedding them, and directly searching a vector store is effectively demoware.
 
 !!! note "What is RAG?"
 Retrival augmented generation (RAG) is a technique that uses an LLM to generate responses, but uses a search backend to augment the generation. In the past year using text embeddings with a vector databases has been the most popular approach I've seen being socialized.