diff --git a/templates/rag-gemini-multi-modal/README.md b/templates/rag-gemini-multi-modal/README.md
index 49d736fb0f7..8d890ca440a 100644
--- a/templates/rag-gemini-multi-modal/README.md
+++ b/templates/rag-gemini-multi-modal/README.md
@@ -1,13 +1,15 @@
 
 # rag-gemini-multi-modal
 
-Multi-modal LLMs enable text-to-image retrieval and question-answering over images. 
+Multi-modal LLMs enable visual assistants that can perform question-answering about images. 
 
-You can ask questions in natural language about a collection of photos, retrieve relevant ones, and have a multi-modal LLM answer questions about the retrieved images.
+This template create a visual assistant for slide decks, which often contain visuals such as graphs or figures.
 
-This template performs text-to-image retrieval for question-answering about a slide deck, which often contains visual elements that are not captured in standard RAG.
+It uses OpenCLIP embeddings to embed all of the slide images and stores them in Chroma.
  
-This will use OpenCLIP embeddings and [Google Gemini](https://deepmind.google/technologies/gemini/#introduction) for answer synthesis.
+Given a question, relevat slides are retrieved and passed to [Google Gemini](https://deepmind.google/technologies/gemini/#introduction) for answer synthesis.
+ 
+![mm-mmembd](https://github.com/langchain-ai/langchain/assets/122662504/b9e69bef-d687-4ecf-a599-937e559d5184)
 
 ## Input
 
@@ -112,4 +114,4 @@ We can access the template from code with:
 from langserve.client import RemoteRunnable
 
 runnable = RemoteRunnable("http://localhost:8000/rag-gemini-multi-modal")
-```
\ No newline at end of file
+```