diff --git a/pages/techniques/ape.en.mdx b/pages/techniques/ape.en.mdx
index dfe144f..e613e66 100644
--- a/pages/techniques/ape.en.mdx
+++ b/pages/techniques/ape.en.mdx
@@ -14,7 +14,7 @@ The first step involves a large language model (as an inference model) that is g
 
 APE discovers a better zero-shot CoT prompt than the human engineered "Let's think step by step" prompt ([Kojima et al., 2022](https://arxiv.org/abs/2205.11916)).
 
-The prompt "Let's work this out it a step by step to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks:
+The prompt "Let's work this out in a step by step way to be sure we have the right answer." elicits chain-of-though reasoning and improves performance on the MultiArith and GSM8K benchmarks:
 
 <Screenshot src={APECOT} alt="APECOT" />
 Image Source: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910)
@@ -23,4 +23,4 @@ This paper touches on an important topic related to prompt engineering which is
 
 - [AutoPrompt](https://arxiv.org/abs/2010.15980) - proposes an approach to automatically create prompts for a diverse set of tasks based on gradient-guided search.
 - [Prefix Tuning](https://arxiv.org/abs/2101.00190) - a lightweight alternative to fine-tuning that prepends a trainable continuous prefix for NLG tasks. 
-- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through backpropagation.
\ No newline at end of file
+- [Prompt Tuning](https://arxiv.org/abs/2104.08691) - proposes a mechanism for learning soft prompts through backpropagation.