diff --git a/fern/pages/get-started/frequently-asked-questions.mdx b/fern/pages/get-started/frequently-asked-questions.mdx
index 133cfa19..4e6b8e04 100644
--- a/fern/pages/get-started/frequently-asked-questions.mdx
+++ b/fern/pages/get-started/frequently-asked-questions.mdx
@@ -13,7 +13,7 @@ Here, we'll walk through some common questions we get about how Cohere's models
 
 ## Cohere Models 
 <AccordionGroup>
-<Accordion title="What is the difference between Command R+ and other Command models?">
+<Accordion title="What is the difference between the Command R and Command R+ models?">
 
 Command R+ is most suitable for those workflows that lean on complex RAG functionality and multi-step tool use (agents). Command R, on the other hand, is great for simpler retrieval augmented generation (RAG) and single-step tool use tasks, as well as applications where price is a major consideration. We offer a full model overview in our [documentation](https://docs.cohere.com/docs/models).
 
@@ -27,7 +27,7 @@ You can check out [this link](https://cohere.com/research/aya) to learn more abo
 
 </Accordion>
 
-<Accordion title="How does Cohere's language model compare to other LLMs like GPT-4, ChatGPT, Claude, Gemini, and others?">
+<Accordion title="How do Cohere's models compare to other LLMs on the market?">
 
 Cohere’s Command models have strong performance across enterprise tasks such as summarization, multilingual use cases, and retrieval augmented generation. We also have the widest range of deployment options, you can check it [here](https://cohere.com/deployment-options). 
 
@@ -70,7 +70,7 @@ You can find the best practices for preparing and structuring fine-tuning data a
 
 <Accordion title="What models are available for fine-tuning using the Cohere platform?">
 
-We support fine-tuning for Command R, Command R 082024 (to be launched October 1st) on the generative side, and for Classify and Rerank models on the representation side.
+On the generative side we support fine-tuning for Command R and Command R 082024. On the representation side, we support fine-tuning for Classify and Rerank models. You can learn more about it [in this section](https://docs.cohere.com/docs/fine-tuning) of our docs. 
 
 </Accordion>
 
@@ -152,7 +152,7 @@ You can find the updated cloud support listed in our [documentation](https://doc
 
 <Accordion title="What are the options and availability for on-premises deployment of Cohere's models?">
 
-We have the ability to deploy all of our models privately. Please reach out to the sales team to learn more.
+We have the ability to deploy all of our models privately. To learn more, please reach out to the sales team [using this form](https://cohere.com/contact-sales).
 
 </Accordion>
 
@@ -164,19 +164,19 @@ Please reach out to the sales team to learn more.
 
 <Accordion title="What are the deployment options and considerations for Cohere models?">
 
-Please reach out to the sales team to learn more.
+To learn more, please reach out to the sales team [using this form](https://cohere.com/contact-sales).
 
 </Accordion>
 
 <Accordion title="Is the licensing for self-deployed models non-commercial or research-only?">
 
-The default license for our open weights is for non-commercial use. For information about licensing please reach out to the sales team to learn more.
+The default license for our open weights is for non-commercial use. For information about licensing please reach out to the sales team [using this form](https://cohere.com/contact-sales).
 
 </Accordion>
 
 <Accordion title="What are the requirements, resources, and methods needed to deploy Cohere models, especially when dealing with specific use cases, confidentiality, and resource constraints?">
 
-Please check our deployment options [here](https://cohere.com/deployment-options) and contact our sales team to learn more.
+Please check our deployment options [here](https://cohere.com/deployment-options) and contact our sales team [with this form](https://cohere.com/contact-sales) to learn more.
 
 </Accordion>
 </AccordionGroup>
@@ -244,7 +244,7 @@ You can find the resources as follows:
 
 For learning, we recommend our [LLM University](https://cohere.com/llmu) hub resources, which have been prepared by Cohere experts. These include a number of very high-quality, step-by-step guides to help you start building quickly. 
 
-For building, we recommend checking out our [Github Notebooks](https://github.com/cohere-ai/notebooks), as well as [Get Started](https://docs.cohere.com/docs/the-cohere-platform), and [Cookbooks](https://docs.cohere.com/page/cookbooks) guides in our documentation.
+For building, we recommend checking out our [Github Notebooks](https://github.com/cohere-ai/notebooks), as well as the [Get Started](https://docs.cohere.com/docs/the-cohere-platform) and [Cookbooks](https://docs.cohere.com/page/cookbooks) sections in our documentation.
 
 </Accordion>
 
@@ -254,7 +254,7 @@ You can access Command with tools using our [Chat](https://chat.cohere.com/) dem
 
 </Accordion>
 
-<Accordion title="What are some best practices and techniques for prompt engineering, specifically when incorporating provided documents into a chat model's response?">
+<Accordion title="What are some best practices and techniques for prompt engineering, specifically when incorporating documents into a chat model's response?">
 
 For general recommendations on prompt engineering check the following resources:
 - [Prompt Engineering Basics](https://cohere.com/llmu/prompt-engineering-basics) Guide
@@ -516,7 +516,7 @@ We also regularly share insights and best practices on AI security on our blog.
 
 </Accordion>
 
-<Accordion title="What if I have More questions?">
+<Accordion title="What if I have more questions?">
 
 If there's anything not covered in this document, you're welcome to reach to us with [this form](https://forms.gle/Mwbn42rrv5vokwFg6).