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-rw-r--r--content/projects/retrieval-augmented-generation.md11
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@@ -20,11 +20,12 @@ source models. This was achieved with [`ollama`](https://ollama.com) and
downloading and running these models locally.
However, the project was expanded to
-integrate with cohere and their rerank and command-r+ models, since I was especially
-curious about the command-r+'s performance. These models can be downloaded and run
-locally, but it took ages for my computer to generate any output, since the command-r+
-model is 104B parameters. The obvious and impressive benefit of the command-r+ is that
-it generates citations from the context in its answer.
+integrate with cohere and their rerank and
+[command-r+](https://cohere.com/blog/command-r-plus-microsoft-azure) models, since I
+was especially curious about the command-r+'s performance. These models can be
+downloaded and run locally, but it took ages for my computer to generate any output,
+since the command-r+ model is 104B parameters. The obvious and impressive benefit of the
+command-r+ is that it generates citations from the context in its answer.
Here is a [presentation](/rag.html) that gives a brief overview of what a RAG system
is, and how it can be improved with reranking.