chore: remove enrollment button snippet

README.md

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 ## License
 
-This is educational material for the "Systematically Improving RAG Applications" course.
+This is educational material for the "Systematically Improving RAG Applications" course.

cohort_1/README.md

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cohort_1/week1_bootstrap_evals/README.md

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cohort_1/week2_question_classification/classify_with_yaml/readme.md

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cohort_1/week2_question_classification/example_notebook/README.md

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cohort_1/week3_seperate_indices/README.md

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cohort_1/week4_routing/README.md

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cohort_1/week5_fine_tuning/README.md

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cohort_2/README.md

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cohort_2/office-hours/README.md

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cohort_2/office-hours/week1-summary.md

@@ -324,8 +324,4 @@ Even with one-to-one mappings, MRR (Mean Reciprocal Rank) is still useful to see
 
 ## What really matters isn't the absolute number but whether we can move these metrics in a positive direction with our interventions. It's like weighing yourself—the absolute number may vary by scale, but if you've gained two pounds, you've definitely gained two pounds.
 
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cohort_2/office-hours/week2-summary.md

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cohort_2/office-hours/week3-summary.md

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 ## _Note: I'll continue to add resources and notes from future office hours sessions_
 
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cohort_2/office-hours/week3/week3-summary.md

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 ## _Note: I'll continue to add resources and notes from future office hours sessions_
 
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cohort_2/office-hours/week4-summary.md

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cohort_2/week0/README.md

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cohort_2/week0/uv.md

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cohort_2/week1/README.md

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cohort_2/week2/README.md

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 ## Both approaches show significant performance gains (15-30% in MRR and recall), highlighting that domain-specific fine-tuning is one of the most effective ways to improve RAG system performance.
 
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cohort_2/week4/README.md

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 ## This data-driven approach helps identify which query areas need improvement most urgently, allowing targeted enhancements rather than making random changes.
 
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cohort_2/week5/README.md

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 ## By combining vector search with structured filtering, SQL access, and document parsing, you'll build RAG systems capable of handling sophisticated user needs that pure semantic search can't address alone.
 
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cohort_2/week6/README.md

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 ## This approach demonstrates how simple prompting changes can dramatically improve tool selection, enabling RAG systems to coordinate multiple specialized capabilities rather than relying on retrieval alone.
 
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docs/blog.md

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docs/office-hours/AGENTS.md

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docs/office-hours/cohort2/week1-summary.md

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docs/office-hours/cohort2/week2-summary.md

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docs/office-hours/cohort2/week3-summary.md

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docs/office-hours/cohort2/week4-summary.md

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docs/office-hours/cohort2/week5-summary.md

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docs/office-hours/cohort2/week6-summary.md

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docs/office-hours/cohort3/week-4-2.md

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docs/office-hours/cohort3/week-5-1.md

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docs/office-hours/cohort3/week-5-2.md

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docs/office-hours/faq.md

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 ## How would you evaluate the effect of different parsing strategies in RAG, notably on documents with weird layouts, tables, and charts?
 
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docs/snippets/README.md

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 Simple enrollment button without additional styling:
 
 ```markdown
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 ### 2. `enrollment-section.md`

docs/talks/AGENTS.md

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docs/talks/chromadb-anton-chunking.md

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 ## By focusing on these fundamentals rather than blindly applying frameworks or following defaults, you can significantly improve the performance of your RAG applications and deliver better results to your users.
 
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docs/talks/colin-rag-agents.md

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 ## This doesn't mean embedding models aren't valuable—they become essential for larger codebases, less structured content, or more complex retrieval tasks.
 
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docs/talks/embedding-performance-generative-evals-kelly-hong.md

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docs/talks/extend-document-automation.md

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docs/talks/fine-tuning-rerankers-embeddings-ayush-lancedb.md

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 ## Multimodal retrieval is likely the next frontier. While much work has been done on text retrieval, there's still significant room for improvement in retrieving and ranking content across different modalities like images, audio, and video. Building better benchmarks and baselines for multimodal RAG systems represents an important area for future development.
 
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docs/talks/glean-manav.md

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 The emphasis on company-specific language models is particularly noteworthy - this is an area where many companies struggle when they try to apply generic embedding models to their unique terminology and document structures.
 
 **Key Takeaway:** Successful enterprise search requires a pragmatic approach that combines custom embedding models, unified data architecture, hybrid search techniques, and continuous learning from user feedback rather than relying solely on off-the-shelf solutions.
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docs/talks/john-lexical-search.md

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 ## **_Key Takeaway:_** The debate isn't really about lexical versus semantic search, but rather how to effectively combine them to create retrieval systems that are both precise and understanding - offering both the filtering power of traditional search and the semantic comprehension of modern embedding-based approaches.
 
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