Fix typo

04-prompt-engineering-fundamentals/README.md

@@ -81,7 +81,7 @@ To get an intuition for how tokenization works, try tools like the [OpenAI Token
 
 ### Concept: Foundation Models
 
-Once a prompt is tokenized, the primary function of the ["Base LLM"](https://blog.gopenai.com/an-introduction-to-base-and-instruction-tuned-large-language-models-8de102c785a6?WT.mc_id=academic-105485-koreyst) (or Foundation model) is to predict the token in that sequence. Since LLMs are trained on massive text datasets, they have a good sense of the statistical relationships between tokens and can make that prediction with some confidence. Not that they don't understand the _meaning_ of the words in the prompt or token; they just see a pattern they can "complete" with their next prediction. They can continue predicting the sequence till terminated by user intervention or some pre-established condition.
+Once a prompt is tokenized, the primary function of the ["Base LLM"](https://blog.gopenai.com/an-introduction-to-base-and-instruction-tuned-large-language-models-8de102c785a6?WT.mc_id=academic-105485-koreyst) (or Foundation model) is to predict the token in that sequence. Since LLMs are trained on massive text datasets, they have a good sense of the statistical relationships between tokens and can make that prediction with some confidence. Note that they don't understand the _meaning_ of the words in the prompt or token; they just see a pattern they can "complete" with their next prediction. They can continue predicting the sequence till terminated by user intervention or some pre-established condition.
 
 Want to see how prompt-based completion works? Enter the above prompt into the Azure OpenAI Studio [_Chat Playground_](https://oai.azure.com/playground?WT.mc_id=academic-105485-koreyst) with the default settings. The system is configured to treat prompts as requests for information - so you should see a completion that satisfies this context.