Merge pull request #663 from jannesDoege/patch-1

Fixed typo in ai-sql-accuracy-2023-08-17.md

papers/ai-sql-accuracy-2023-08-17.md

@@ -229,7 +229,7 @@ By providing just those 3 example queries, we see substantial improvements to th
 
 Enterprise data warehouses often contain 100s (or even 1000s) of tables, and an order of magnitude more queries that cover all the use cases within their organizations. Given the limited size of the context windows of modern LLMs, we can’t just shove all the prior queries and schema definitions into the prompt.
 
-Our final approach to context is a more sophisticated ML approach - load embeddings of prior queries and the table schemas into a vector database, and only choose the most relevant queries / tables to the question asked. Here's a diagram of what we are doing - note the contextual relevance search in the red box -
+Our final approach to context is a more sophisticated ML approach - load embeddings of prior queries and the table schemas into a vector database, and only choose the most relevant queries / tables to the question asked. Here's a diagram of what we are doing - note the contextual relevance search in the green box -
 
 ![](https://raw.githubusercontent.com/vanna-ai/vanna/main/papers/img/using-contextually-relevant-examples.png)