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04-prompt-engineering-fundamentals/README.md

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 [![Prompt Engineering Fundamentals](./images/04-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson4-gh?WT.mc_id=academic-105485-koreyst)
 
 ## Introduction
-This module covers essential concepts and techniques for creating effective prompts in generative AI models. The way your write your prompt to an LLM also matters. A carefully-crafted prompt can achieve a better quality of response. But what exactly do terms like _prompt_ and _prompt engineering_ mean? And how do I improve the prompt _input_ that I send to the LLM? These are the questions we'll try to answer with in this chapter and the next.
+This module covers essential concepts and techniques for creating effective prompts in generative AI models. The way you write your prompt to an LLM also matters. A carefully-crafted prompt can achieve a better quality of response. But what exactly do terms like _prompt_ and _prompt engineering_ mean? And how do I improve the prompt _input_ that I send to the LLM? These are the questions we'll try to answer within this chapter and the next.
 
 _Generative AI_ is capable of creating new content (e.g., text, images, audio, code etc.) in response to user requests. It achieves this using _Large Language Models_ like OpenAI's GPT ("Generative Pre-trained Transformer") series that are trained for using natural language and code.
 
@@ -150,7 +150,7 @@ So what happens when we run this prompt with different LLM providers?
 
 ![Response 3](./images/04-fabrication-huggingchat.png?WT.mc_id=academic-105485-koreyst)
 
-As expected, each model (or model version) produces slightly different responses thanks to stochastic behavior and model capability variations. For instance, one model targets an 8th grade audience while the other assumes a high-school student. But all three models did generate responses that could convince an uninformed user that the event was real
+As expected, each model (or model version) produces slightly different responses thanks to stochastic behavior and model capability variations. For instance, one model targets an 8th grade audience while the other assumes a high-school student. But all three models did generate responses that could convince an uninformed user that the event was real.
 
 Prompt engineering techniques like _metaprompting_ and _temperature configuration_ may reduce model fabrications to some extent. New prompt engineering _architectures_ also incorporate new tools and techniques seamlessly into the prompt flow, to mitigate or reduce some of these effects.
 
@@ -401,4 +401,3 @@ See if you can leverage the "cue" technique with the prompt: Complete the senten
 Want to learn more about different Prompt Engineering concepts? Go to the [continued learning page](https://aka.ms/genai-collection?WT.mc_id=academic-105485-koreyst) to find other great resources on this topic.
 
 Head over to Lesson 5 where we will look at [advanced prompting techniques](../05-advanced-prompts/README.md?WT.mc_id=academic-105485-koreyst)!
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