diff --git a/website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx b/website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx
index 7e77db8f5910..924ca4eb3525 100644
--- a/website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx
+++ b/website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx
@@ -16,7 +16,7 @@ In this blog post, we will explore a creative, adaptive way of using GPT models
 
 ## Observations
 
-* GPT-3.5-Turbo can alrady solve 40%-50% tasks. For these tasks if we never use GPT-4, we can save nearly 40-50% cost.
+* GPT-3.5-Turbo can already solve 40%-50% tasks. For these tasks if we never use GPT-4, we can save nearly 40-50% cost.
 * If we use the saved cost to generate more responses with GPT-4 for the remaining unsolved tasks, it is possible to solve some more of them while keeping the amortized cost down.
 
 The obstacle of leveraging these observations is that we do not know *a priori* which tasks can be solved by the cheaper model, which tasks can be solved by the expensive model, and which tasks can be solved by paying even more to the expensive model.