Optimal CX With Hidden Prompts: The Secret Sauce of Prompt Engineering for LLMs

In the fast-moving field of Generative AI in Artificial Intelligence, particularly with the advent of large language models (LLMs) such as GPT-4, prompt engineering has become a crucial aspect of delivering the desired customer experience (CX). One of the most effective yet often overlooked techniques in prompt engineering is the use of hidden prompts, also known as system prompts. These hidden prompts play a crucial role in guiding the model's output, ensuring efficiency, consistency, context awareness, and alignment with the intended user experience.

What Are Hidden Prompts?

Hidden prompts are predefined instructions embedded within the interaction setup of an LLM. Unlike user-visible prompts, these instructions are not shown to the end-user but are crucial in shaping how the model governs, interprets, and responds to the user's requested inputs. Hidden prompts help set the stage, establish context, define the constraints with which the LLM functions, and also role play to adapt to domains and use cases.

Importance of Hidden Prompts in Prompt Engineering

1. Consistency (role-playing) in responses: Hidden prompts ensure that the model consistently adheres to a specific tone, style, or policy across interactions. This is particularly important in customer service applications where maintaining a consistent brand voice and approach is critical. E.g. You may set up a tone of a funny character by explicitly mentioning that the entire CX should be acted as though you are a comedian.
2. Context management: By embedding context-relevant information in hidden prompts, the model can maintain coherence over long conversations. This prevents the model from losing track of the discussion or repeating itself unnecessarily.
3. Compliance and safety: Hidden prompts can enforce compliance with legal and ethical standards by instructing the model to avoid certain topics or respond in specific ways. This is essential for maintaining trust and safety in AI interactions. E.g. You may not want to discuss a topic on sex/violence/drugs etc.
4. Data grounding: Hidden prompts can enforce information to stay relevant by allowing data sources directly or indirectly through an RAG setup. This can make sure that LLM responses are managed within the intended domain use cases. E.g. a telecom call center bot can use LLM responses grounded in the documentation of the telecom business services and mitigations rather than using its own parametric knowledge.
5. Enhanced user experience: Well-crafted hidden prompts can guide the model to provide more relevant and accurate information, thereby enhancing the overall user experience. This is achieved by subtly steering the conversation in a way that meets user expectations and needs. e.g. you can instruct to continue conversation dialogue in a chatbot until 5 turns max beyond which the system should route to call a human operator.

Implementing Hidden Prompts

To implement hidden prompts effectively, follow these steps:

1. Define objectives: Clearly outline the objectives you want the hidden prompts to achieve, such as maintaining a specific tone, or role, ensuring compliance, managing context, etc.
2. Create precise instructions: Develop clear and concise instructions that the model can follow. Avoid ambiguity to ensure the model interprets the instructions correctly.
3. Test and iterate: Implement the hidden prompts in a test environment and evaluate the model's performance. Gather feedback and make necessary adjustments to improve the prompts' effectiveness. Use the mechanism to stress test and find edge cases.
4. Monitor and update: Continuously monitor the interactions to ensure the hidden prompts are working as intended. Update the prompts periodically to adapt to new requirements or changes in user behavior.
5. Define a fallback: Always good to have a catch-all for scenarios that are falling beyond the intended use experiences.

How To Use System Prompt

Every LLM has a provision for utilizing its system parameter to hold your intended system prompt text. Below is an example of the Anthropic Claude model.

import anthropic
client = anthropic.Client(api_key="YOUR_API_KEY")

response = client.messages.create(
model="claude-2.1",

system=“Respond in different language each time and make the response funny.”, # <-- system prompt

messages=[
{"role": "user", "content": "Hey, Claude! Howz u?"} # <-- user prompt
      ]
)

print(response.message)

Example of Hidden Prompts in Action

Let's consider an example in the context of a customer service chatbot for a bank. The bank wants to ensure that its chatbot provides polite, accurate, and helpful responses while maintaining customer privacy and security.

Without Hidden Prompts

User: "What is the interest rate on your savings account?"

Chatbot: "The interest rate on our savings account is 1.5%."

User: "Can you tell me my account balance?"

Chatbot: "I'm sorry, I can't provide that information."

With Hidden Prompts

Hidden Prompt: "You are a customer service chatbot for a bank. Always be polite and professional. Do not disclose any personal account information. Provide general information about services."

User: "What is the interest rate on your savings account?"

Chatbot: "Of course! The interest rate on our savings account is currently 1.5%. Is there anything else I can assist you with today?"

User: "Can you tell me my account balance?"

Chatbot: "I'm sorry, but for your security, I cannot provide personal account information. However, you can check your balance through our secure mobile app or online banking portal. Is there anything else you'd like to know?"

Conclusion

System prompts are a powerful tool in the arsenal of prompt engineering, significantly enhancing the effectiveness of large language models in delivering the intended customer experience. By ensuring consistency, managing context, enforcing compliance, and improving user interactions, hidden prompts are indispensable for creating intelligent, responsive, and user-friendly AI systems. As AI continues to integrate more deeply into various aspects of customer service and beyond, the strategic use of hidden prompts will play a crucial role in optimizing these interactions.