Update README.md

Author: Sherlock113

README.md

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 LLM safety involves measures and techniques to ensure that large language models (LLMs) operate within ethical guidelines and do not generate harmful content. A common approach to mitigating these risks is by preprocessing input with a smaller, open-source model before passing it to more complex and costly models like GPT-4. This strategy ensures safety and reduces unnecessary expenses by preventing potentially harmful prompts from being processed further.
 
-This document demonstrates how to build an AI assistant using BentoML and [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma) to preemptively filter out harmful input, thereby ensuring LLM safety.
-
-See [here](https://github.com/bentoml/BentoShield/) for the source code.
-
-The example application allows you to set a safety threshold. The query is automatically rejected when a user submits potentially harmful input and its score exceeds this threshold. An example of a harmful query:
+This example demonstrates how to build an AI assistant using BentoML and [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma) to preemptively filter out harmful input, thereby ensuring LLM safety. It allows you to set a safety threshold. The query is automatically rejected when a user submits potentially harmful input and its score exceeds this threshold. An example of a harmful query:
 
 ```json
 {
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 <img width="1580" alt="Screenshot 2024-09-02 at 16 59 37" src="https://github.com/user-attachments/assets/b0b3810d-f35e-4115-8ca2-fc6003abb2fd">
 
+See [here](https://docs.bentoml.com/en/latest/examples/overview.html) for a full list of BentoML example projects.
+
 ## Architecture
 
 This example includes two BentoML Services: `Gemma` and `ShieldAssistant`. `Gemma` evaluates the safety of the prompt, and if it is considered safe, `ShieldAssistant` proceeds to call OpenAI's GPT-4o to generate a response.