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

@@ -411,7 +411,7 @@ Is there a way to always provide *new* hallucinations from the Host model? Yes,
 
 ## Foundations for Privacy
 
-The learning goals of this project were to experiment with working with LLMs locally on hosts where the training data and tuning of a model remains private and only derived information from prompt-based AI chains can be shared with other users/peers for consumption, allowing for working with AI computation in ways not tied to any specific vendor or large cloud provider. Essentially, this showcases a positive avenue for decentralized, user-controlled AI-oriented computation that's everything that [IEEE Spectrum's Open-Source AI Is Uniquely Dangerous][ieee] isn't.
+The learning goals of this project were to experiment with working with LLMs locally on hosts where the training data and tuning of a model remains private and only derived information from prompt-based AI chains can be shared with other users/peers for consumption. AI computation is possible without being tied to a specific vendor or large cloud provider. Essentially, this showcases a positive avenue for decentralized, user-controlled AI-oriented computation that's everything that [IEEE Spectrum's Open-Source AI Is Uniquely Dangerous][ieee] isn't.
 
 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].