Fix minor typo

pages/techniques/prompt_chaining.en.mdx

@@ -19,7 +19,7 @@ Prompt chaining is particularly useful when building LLM-powered conversational
 
 Prompt chaining can be used in different scenarios that could involve several operations or transformations. For instance, one common use case of LLMs involves answering questions about a large text document. It helps if you design two different prompts where the first prompt is responsible for extracting relevant quotes to answer a question and a second prompt takes as input the quotes and original document to answer a given question. In other words, you will be creating two different prompts to perform the task of answering a question given a document.
 
-The first prompt below extracts the relevant quotes from the document given the question. Note that for simplicity, we have added a placeholder for the document `{{document}}`. To test the prompt you can copy and past an article from Wikipedia such as this page for [prompt engineering](https://en.wikipedia.org/wiki/Prompt_engineering). Due to larger context used for this task, we are using the `gpt-4-1106-preview` model from OpenAI. You can use the prompt with other long-context LLMs like Claude. 
+The first prompt below extracts the relevant quotes from the document given the question. Note that for simplicity, we have added a placeholder for the document `{{document}}`. To test the prompt you can copy and paste an article from Wikipedia such as this page for [prompt engineering](https://en.wikipedia.org/wiki/Prompt_engineering). Due to larger context used for this task, we are using the `gpt-4-1106-preview` model from OpenAI. You can use the prompt with other long-context LLMs like Claude. 
 
 Prompt 1: 
 ```