diff --git a/docs/writing/posts/rag-plusplus.md b/docs/writing/posts/rag-plusplus.md
index d4a0bbf..0034933 100644
--- a/docs/writing/posts/rag-plusplus.md
+++ b/docs/writing/posts/rag-plusplus.md
@@ -29,7 +29,6 @@ If you want to learn more about I systematically improve RAG applications check
 
 [Check out the free email course here](https://dub.link/6wk-rag-email){ .md-button .md-button--primary }
 
-
 ## Reports over RAG
 
 So why are reports better than RAG? Simply put, RAG systems suck because the value you derive is time saved from finding an answer. This is a one-dimensional value, and it's very hard to sell any value beyond that. Meanwhile, a report is a higher-value product because it is a decision-making tool that enables better resource allocation.
diff --git a/docs/writing/posts/rag-six-tips-improving.md b/docs/writing/posts/rag-six-tips-improving.md
index f7c4266..d4a8658 100644
--- a/docs/writing/posts/rag-six-tips-improving.md
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@@ -17,14 +17,6 @@ tags:
 
 This article explains six proven strategies to improve Retrieval-Augmented Generation (RAG) systems. It builds on my previous articles and consulting experience helping companies enhance their RAG applications.
 
-In [RAG is More Than Just Embeddings](../posts/rag.md), I explain how RAG goes beyond vector embeddings. I also wrote [How to Build a Terrible RAG System](../posts/rag-inverted.md), which shows what not to do - helping you learn good practices through inverted thinking.
-
-For a deeper understanding of RAG complexity, check out [Levels of RAG Complexity](../posts/rag-levels-of-rag.md). This article breaks down RAG into manageable components. If you want quick wins, read [Low Hanging Fruit in RAG](../posts/rag-low-hanging-fruit.md).
-
-I've also written about the future of RAG in [Predictions for the Future of RAG](../posts/rag-plusplus.md), exploring how RAG may evolve into report generation.
-
-These articles work together to provide a comprehensive guide on RAG systems. They offer practical tips for developers and organizations looking to improve their implementations.
-
 By the end of this post, you'll understand six key strategies I've found effective when improving RAG applications:
 
 - Building a data flywheel with synthetic testing
@@ -34,8 +26,6 @@ By the end of this post, you'll understand six key strategies I've found effecti
 - Leveraging metadata effectively
 - Creating robust feedback loops
 
-Let's explore each of these strategies in detail and see how they can help you build better RAG systems.
-
 <!-- more -->
 
 If you want to learn more about I systematically improve RAG applications check out my free 6 email improving rag crash course 
@@ -47,6 +37,7 @@ If you want to learn more about I systematically improve RAG applications check
 One of the most common pitfalls in RAG development is relying on "looks good to me" testing instead of systematic evaluation. The solution? Start with synthetic testing data.
 
 **Key Implementation Steps:**
+
 - Generate at least 100 diverse test cases covering your expected use cases
 - Focus on retrieval metrics (precision/recall) over generation quality
 - Begin with synthetic data, then gradually blend in real user feedback
diff --git a/docs/writing/posts/rag.md b/docs/writing/posts/rag.md
index 5e4c194..5b9595d 100644
--- a/docs/writing/posts/rag.md
+++ b/docs/writing/posts/rag.md
@@ -34,6 +34,10 @@ So let's kick things off by examining what I like to call the 'Dumb' RAG Model
 
 <!-- more -->
 
+If you want to learn more about I systematically improve RAG applications check out my free 6 email improving rag crash course 
+
+[Check out the free email course here](https://dub.link/6wk-rag-email){ .md-button .md-button--primary }
+
 ## The 'Dumb' RAG Model
 
 When you ask a question like, "what is the capital of France?" The RAG 'dumb' model embeds the query and searches in some unopinonated search endpoint. Limited to a single method API like `search(query: str) -> List[str]`. This is fine for simple queries, since you'd expect words like 'paris is the capital of france' to be in the top results of say, your wikipedia embeddings.
@@ -237,3 +241,7 @@ To deepen your understanding of RAG systems and their implementation, explore th
 - [RAG Beyond Embeddings](./rag.md) - Explore the broader scope of RAG beyond vector embeddings
 
 These resources offer valuable perspectives on building, optimizing, and maintaining effective RAG systems in various contexts.
+
+If you want to learn more about I systematically improve RAG applications check out my free 6 email improving rag crash course 
+
+[Check out the free email course here](https://dub.link/6wk-rag-email){ .md-button .md-button--primary }
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