A significant portion of social media users have experienced at least one frustrating chatbot interaction online. One major reason for this frustration is that most chatbot responses are rigidly designed using predefined conversation flows. Users must follow a specific sequence to obtain the information they need, which can feel like navigating a maze. This often results in users going in circles, hitting dead ends, and ultimately giving up and reverting to calling a hotline or emailing customer support.
Similarly, using zero-shot prompting with models like GPT-4o can have limitations, as these models might not consistently understand and respond to diverse and complex queries effectively. It’s the same way as prompting ChatGPT on the first attempt, you always get mixed results. You can refine the answer by having a follow-up prompt that can help ChatGPT reflect and refine its answer. This is why an agentic approach, such as multi-agent design, is more appropriate.
Providing seamless and accurate interactions has become a cornerstone for enhancing user experience in the rapidly evolving landscape of chatbot technologies. At ChatGenie, we recognized the need to improve our direct message and public comments auto-responses to meet the growing expectations of both businesses and their customers. We implemented a multi-agent design that ensures our chatbot responses are not only accurate and relevant but also safe and secure. This article will discuss why and how we designed and implemented this approach.
Why Multi-Agent Design?
By adopting a multi-agent design, we address these challenges head-on:
- Specialization: Different agents can specialize in distinct tasks, ensuring each aspect of the interaction is handled by an expert system.
- Scalability: Multi-agent systems can scale more effectively, managing higher volumes of interactions without compromising performance.
- Robustness: Distributing tasks across multiple agents reduces the risk of system-wide failures, enhancing overall reliability.
Our implementation process involved several critical steps to ensure the success of our multi-agent design:
Llama3 serves as the foundational model for our multi-agent system, chosen for its advanced natural language understanding and generation capabilities. Key advantages of Llama3 include:
- Contextual Awareness: Ability to understand and maintain context across interactions.
- Efficiency: Smaller context length compared to models like GPT-4, making it ideal for simpler tasks and faster processing.
- Localization: Robust training data in multiple languages, including Filipino, ensuring effective handling of localized content.
We divided the responsibilities among specialized agents:
- Classification Agent: Analyzes the type of inquiry the customer has based on the message sent to the business, ensuring the appropriate handling of different types of queries.
- Prompt Guard Agent: Ensures safety by filtering offensive content, protecting sensitive information, preventing misinformation, and maintaining security. See this article in how ChatGenie Prompt Guard works: https://chatgenie.ph/post/ensuring-safe-and-accurate-chatgenie-chatbot-interactions-with-llama3
- Response Refinement Agent: Enhances quality by ensuring responses are accurate, clear, and contextually appropriate. This agent also maintains tone and style consistency and encourages user engagement.
Our system integrates these specialized agents seamlessly, coordinating their efforts to provide a unified user experience. The process includes:
- Input Analysis: The Prompt Guard Agent first analyzes user input for safety and relevance.
- Response Generation: The Response Refinement Agent then generates a refined and contextually appropriate response.
- Feedback Loop: Continuous feedback from user interactions helps fine-tune the agents, improving their performance over time.
To illustrate how this design works in action, we prepared an animated conversation between a customer and the business chatbot powered by ChatGenie
To add more context to future conversations, every detail that was in the conversations is saved in the customer profile which will serve as memory.
Throughout the design and implementation process, we prioritized compliance with legal and ethical standards. This includes adherence to data protection regulations like GDPR and CCPA, and ensuring our system avoids biases and maintains cultural sensitivity.
This is just one example of how we are applying LLMs to improve commerce experience within social media and messaging platforms and we are just getting started.
Are you ready to elevate your chatbot capabilities with cutting-edge AI technology? Discover how ChatGenie’s multi-agent design using Llama3 can revolutionize your direct message and public comments auto-responses. Fill out the form below to inquire and take the first step towards transforming your user interactions.