Update README.md

Fixed some potential misspellings.

Author: jun216tee

01-introduction-to-genai/README.md

@@ -4,7 +4,7 @@
 
 _(Click the image above to view video of this lesson)_
 
-Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language. There's no need for you to learn a language like Java or SQL to accomplish something worthwhile, all you need is to use your language, state what you want and out comes a suggestion from an AI model. The applications and impact for this is huge, you write or understand reports, write applications and much more, all in seconds.
+Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language. There's no need for you to learn a language like Java or SQL to accomplish something worthwhile, all you need is to use your language, state what you want and out comes a suggestion from an AI model. The applications and impact for this are huge, you write or understand reports, write applications and much more, all in seconds.
 
 In this curriculum, we’ll explore how our startup leverages generative AI to unlock new scenarios in the education world and how we address the inevitable challenges associated with the social implications of its application and the technology limitations.
 
@@ -40,7 +40,7 @@ To start, let’s define some basic concepts and terminology we’ll be using th
 
 ## How did we get Generative AI?
 
-Despite the extraordinary _hype_ created lately by the announcement of generative AI models, this technology is decades in the making, with the first research efforts dating back to 60s. We're now at a point with AI having human cognitive capabilities, like conversation as shown by for example [OpenAI ChatGPT](https://openai.com/chatgpt) or [Bing Chat](https://www.microsoft.com/edge/features/bing-chat?WT.mc_id=academic-105485-koreyst), which also uses a GPT model for the web search Bing conversations.
+Despite the extraordinary _hype_ created lately by the announcement of generative AI models, this technology is decades in the making, with the first research efforts dating back to the 60s. We're now at a point with AI having human cognitive capabilities, like conversation as shown by for example [OpenAI ChatGPT](https://openai.com/chatgpt) or [Bing Chat](https://www.microsoft.com/edge/features/bing-chat?WT.mc_id=academic-105485-koreyst), which also uses a GPT model for the web search Bing conversations.
 
 Backing up a bit, the very first prototypes of AI consisted of typewritten chatbots, relying on a knowledge base extracted from a group of experts and represented into a computer. The answers in the knowledge base were triggered by keywords appearing in the input text.
 However, it soon became clear that such an approach, using typewritten chatbots, did not scale well.
@@ -77,15 +77,15 @@ In the next chapter we are going to explore different types of Generative AI mod
 
 - **Predicting output tokens**: Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern, enabling a better user experience of getting one (or multiple) sentence as an answer. This explains why, if you ever played with ChatGPT, you might have noticed that sometimes it looks like it stops in the middle of a sentence.
 
-- **Selection process, probability distribution**: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always the token with the highest probability is chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature.
+- **Selection process, probability distribution**: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always is the token with the highest probability chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature.
 
 ## How can our startup leverage Large Language Models?
 
 Now that we have a better understanding of the inner working of a large language model, let’s see some practical examples of the most common tasks they can perform pretty well, with an eye to our business scenario.
 We said that the main capability of a Large Language Model is _generating a text from scratch, starting from a textual input, written in natural language_.
 
 But what kind of textual input and output?
-The input of a large language model is known as prompt, while the output is known as completion, term that refers to the model mechanism of generating the next token to complete the current input. We are going to dive deep into what is a prompt and how to design it in a way to get the most out of our model. But for now, let’s just say that a prompt may include:
+The input of a large language model is known as a prompt, while the output is known as a completion, term that refers to the model mechanism of generating the next token to complete the current input. We are going to dive deep into what is a prompt and how to design it in a way to get the most out of our model. But for now, let’s just say that a prompt may include:
 
 - An **instruction** specifying the type of output we expect from the model. This instruction sometimes might embed some examples or some additional data.
 
@@ -109,7 +109,7 @@ The input of a large language model is known as prompt, while the output is know
 
   ![Coding example](./images/coding-example.png?WT.mc_id=academic-105485-koreyst)
 
-The examples above are quite simple and don’t want to be an exhaustive demonstration of Large Language Models capabilities. They just want to show the potential of using generative AI, in particular but not limited to educational context.
+The examples above are quite simple and don’t want to be an exhaustive demonstration of Large Language Models capabilities. They just want to show the potential of using generative AI, in particular but not limited to educational contexts.
 
 Also, the output of a generative AI model is not perfect and sometimes the creativity of the model can work against it, resulting in an output which is a combination of words that the human user can interpret as a mystification of reality, or it can be offensive. Generative AI is not intelligent - at least in the more comprehensive definition of intelligence, including critical and creative reasoning or emotional intelligence; it is not deterministic, and it is not trustworthy, since fabrications, such as erroneous references, content, and statements, may be combined with correct information, and presented in a persuasive and confident manner. In the following lessons, we’ll be dealing with all these limitations and we’ll see what we can do to mitigate them.
 
@@ -127,7 +127,7 @@ What's true about large language models?
 1. It does things perfectly, great at adding numbers, produce working code etc.
 1. The response may vary despite using the same prompt. It's also great at giving you a first draft of something, be it text or code. But you need to improve on the results.
 
-A: 3, an LLM is non-deterministic, the response vary, however, you can control its variance via a temperature setting. You also shouldn't expect it to do things perfectly, it's here to do the heavy-lifting for you which often means you get a good first attempt at something that you need to gradually improve.
+A: 3, an LLM is non-deterministic, the response varies, however, you can control its variance via a temperature setting. You also shouldn't expect it to do things perfectly, it's here to do the heavy-lifting for you which often means you get a good first attempt at something that you need to gradually improve.
 
 ## Great Work! Continue the Journey