diff --git a/lessons/05-rag/README.md b/lessons/05-rag/README.md
index 343234a..f734c2a 100644
--- a/lessons/05-rag/README.md
+++ b/lessons/05-rag/README.md
@@ -34,7 +34,7 @@ _This video explains Retrieval Augmented Generation (RAG), a method that helps t
 
 **Leonardo:** "Genesis? Che cosa significa?" Leonardo asked, confused. Before you could respond, the world dissolved into a blur of colors and sounds, the temple fading away as you were pulled through time
 
-You land in garden, it's late at night with a thick fog and eerie lights are flickering in the distance. The mansion looms before you. Leonardo looks around, his eyes wide with wonder.
+You land in the garden, it's late at night with a thick fog and eerie lights are flickering in the distance. The mansion looms before you. Leonardo looks around, his eyes wide with wonder.
 
 <div>
   <img src="./assets/mansion.jpeg" alt="Old mansion shown in a deep fog" width="300">
@@ -103,11 +103,11 @@ For a more detailed explanation of the app, see [Detailed app explanation](../01
 
 First, you need to address some challenges before diving into RAG details. These models, trained on vast text data, can produce relevant and correct responses. But, like any data source, their output can be inaccurate, incomplete, or misleading due to various factors.
 
-- **Out of date sources:** The data used to train the model may be outdated and no longer accurate.
+- **Out-of-date sources:** The data used to train the model may be outdated and no longer accurate.
 - **Wrong or inaccurate information:** The sources used to train the model may contain incorrect or misleading information, like fake news or biased opinions.
 - **Non-authoritative sources:** The model may not be able to distinguish between authoritative and non-authoritative sources in its training data, leading to unreliable information.
 
-This makes it difficult to tell if an information generated by an LLM is correct or not. This is where RAG comes in.
+This makes it difficult to tell if the information generated by an LLM is correct or not. This is where RAG comes in.
 
 **You:** So I need to make sure the device can provide accurate information, even when it's not sure about the answer?
 
@@ -157,7 +157,7 @@ Once you've settled on a method for integrating external data sources, you may a
 > [!NOTE]
 > When integrating external data sources into your AI application, it's important to consider the privacy and security implications of accessing and storing data. Make sure you have the necessary permissions and safeguards in place to protect the data and comply with any relevant regulations.
 
-If you're using a database, you also want to think how you want to *search your data* to retrieve the most relevant information. This can be done using keyword search, full-text search, or more advanced techniques like semantic search or vector search that may need specific indexing. We'll cover advanced search techniques in a future lesson.
+If you're using a database, you also want to think about how you want to *search your data* to retrieve the most relevant information. This can be done using keyword search, full-text search, or more advanced techniques like semantic search or vector search that may need specific indexing. We'll cover advanced search techniques in a future lesson.
 
 **You**: Can you explain terms like API and Databases in more 1860s terms?
 
@@ -282,7 +282,7 @@ for await (const chunk of chunks) {
 
 You can find this code in the [`example/rag-cars.js`](./example/rag-cars.js) file along with the [`hybrid.csv`](./example/hybrid.csv) file containing the data used for the retrieval.
 
-**Ada:** Once you run this code, you should see the data found in the CSV file by the retriever, formatted as a markdown table, followed by the AI-generated response to the question. Try changing the question to see how the retrieved data and response changes. You can also try asking questions about unrelated topics to see how the AI model handles them.
+**Ada:** Once you run this code, you should see the data found in the CSV file by the retriever, formatted as a markdown table, followed by the AI-generated response to the question. Try changing the question to see how the retrieved data and response change. You can also try asking questions about unrelated topics to see how the AI model handles them.
 
 ```text
 Example of the output:
@@ -344,4 +344,4 @@ C. The retriever finds relevant information from external data sources.
 - **Sample apps**:
   * [Serverless AI Chat with RAG](https://github.com/Azure-Samples/serverless-chat-langchainjs/)
   * [Ask Youtube: A RAG-based Youtube Q&A API](https://github.com/Azure-Samples/langchainjs-quickstart-demo)
-- [Full length workshop: Create your own ChatGPT with RAG](https://moaw.dev/workshop/gh:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md)
+- [Full-length workshop: Create your own ChatGPT with RAG](https://moaw.dev/workshop/gh:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md)