Update example-llm-workflows/README.md

Co-authored-by: Brian Ginsburg <[email protected]>
Signed-off-by: Zeeshan Lakhani <[email protected]>

example-llm-workflows/README.md

@@ -415,7 +415,7 @@ The learning goals of this project were to experiment with working with LLMs loc
 
 Localized, open-source LLM models for home and on-prem use cases are growing in popularity, even leading to the creation of a [LocalLLaMA reddit][reddit-post] community! We've seen how [GDPR][gdpr] has increased the need for companies to be more careful around [PII management and data privacy isolation across regions and region-compliance laws][so-privacy].
 
-IP is also a concern for companies, as they want to protect proprietary data, including trained models of their own. Some companies have the funding to pay OpenAI or other cloud AI provider platforms to work with their data through private channels, but not every company can afford this; nor does this present security against a massive IP data leak.
+IP is also a concern for companies, where they want to protect proprietary data, including trained models of their own. Some companies have the funding to pay OpenAI or other cloud AI provider platforms to work with their data through private channels, but not every company can afford this; nor does this present security against a massive IP data leak.
 
 Self-hosted, privately managed model deployments hit on many of the privacy and security modules taught in our course. Incorporating ways for users to chain LLM steps together while controlling what inference gets exhibited without the infrastructure concerns or data risks typically associated with external cloud services, presents a unique opportunity to democratize AI capabilities. By ensuring that users can interact with and execute complex AI
 workflows with ease, this project aims to bridge the gap between advanced AI technologies and those with some software development background. This approach not only aligns with the course's focus on privacy and security, but also empowers users by providing them with tools to leverage AI in a secure, private, and user-friendly manner.