Ensuring Responsible AI on the Application Layer

Table of Contents

1. Introduction
2. Responsible AI on the Data Layer
   • Prompt Engineering
   • Reinforcing Instruction Following
   • Personalization
3. Responsible AI on the Model Layer
4. Responsible AI on the Application Layer
   • Prompt Engineering
   • Guardrails
5. Applying Prompt Engineering for Responsible AI
6. Using Guardrails for Responsible AI
7. Case Study: Knowledge GPT Solution
8. Fact Checking with Gil Algorithm
9. Creating Different Guardrails for Different Use Cases
10. Conclusion

Responsible AI on the Application Layer

In this episode of Gen AI 101, we will be discussing how to think about responsible AI on the application layer. When we talk about the application layer, there are two main aspects to consider: prompt engineering and guardrails.

Prompt Engineering

Prompt engineering plays a crucial role in ensuring responsible AI. Just like in the previous episode where we discussed giving feedback to the model to improve its instruction following capabilities, prompt engineering also requires reinforcing the model to follow instructions precisely. We can achieve this by using specific words and instructions in the prompt.

For example, in our knowledge AI solution known as Knowledge GPT, we integrate with various enterprise knowledge systems such as SharePoint, Knowledge Management, Zenas, and more. When an employee asks a question, we append instructions in the prompt to only answer the question from the knowledge that is in context. This means that the model should not refer to its own knowledge or create any new knowledge unless it is specifically related to the context of the question. By reinforcing the instruction to follow the prompt, we can ensure more responsible AI.

Moreover, personalization also falls within prompt engineering. By entering the company knowledge and employee profiles, the AI model can provide tailored answers based on the individual's access to knowledge articles. For instance, if an employee has access to 5,000 knowledge articles, their search results will differ from someone who has access to 10,000 knowledge articles. By incorporating personalization into prompt engineering, we can ensure that people only receive answers related to the knowledge they have access to.

Guardrails

While prompt engineering focuses on reinforcing instruction following, guardrails come into play after the model has generated a response. Within the application layer, guardrails act as additional checks to ensure responsible AI.

One approach is to implement a fact-checking mechanism using an algorithm called Gil. After the response is created by the AI model, the Gil algorithm examines the entities Mentioned in that response. It then compares these entities with the context provided in the prompt. This comparison helps determine whether the AI model generated the response based on the given prompt or from its own knowledge. By applying the fact-checking process, we can ascertain the origin of the response and maintain responsible AI practices.

It's important to note that guardrails can vary for different use cases. In the case of our Knowledge GPT solution, fact-checking and entity matching are effective guardrails. However, other applications may require different guardrail mechanisms tailored to their specific needs.

In conclusion, responsible AI on the application layer requires prompt engineering to reinforce instruction following and personalization within the prompt. Additionally, the use of guardrails, such as fact-checking algorithms, can enhance responsible AI practices. With these approaches in mind, we can continue to develop AI applications that prioritize responsible and ethical use.

🌟 Highlights

• Prompt engineering is crucial for ensuring responsible AI on the application layer.
• Use specific words and instructions in the prompt to reinforce instruction following.
• Personalization within the prompt enables tailored responses based on individual access to knowledge.
• Guardrails, such as fact-checking algorithms, add an extra layer of checks for responsible AI.
• Different applications may require different guardrails tailored to their specific use cases.

🙋‍♂️ FAQ

Q: How does prompt engineering contribute to responsible AI? A: Prompt engineering helps reinforce instruction following by using specific words and instructions in the prompt. It ensures that AI models only generate responses that align with the given prompt and context.

Q: Can personalization be achieved through prompt engineering? A: Yes, personalization can be incorporated into prompt engineering. By including individual profiles and access to knowledge articles, the AI model can provide tailored responses based on the specific knowledge available to each individual.

Q: What are guardrails in the context of responsible AI? A: Guardrails act as additional checks to ensure responsible AI. They can include mechanisms like fact-checking algorithms, which compare the entities mentioned in AI-generated responses with the context provided in the prompt.

Q: Are guardrails the same for all AI applications? A: No, guardrails may vary for different AI applications. Each application may require specific guardrail mechanisms tailored to its unique use case and requirements.