Work Smarter, Not Harder: Master of Code Global’s AI Toolkit for Conversation Design
Conversation design has been undergoing a transformation for quite a while now. Large Language Models (LLMs) are revolutionizing how we create, refine, and optimize conversational experiences. At Master of Code Global, we've embraced this revolution and integrated AI tools into our conversation design workflow.?
We’ve discovered that LLMs don’t just accelerate our work - they amplify our quality of work. They help with research and generating fresh content ideas. This lets us focus on creating conversations that resonate with both users and clients.
This post will give you an inside look into how we’re integrating AI tools into our conversation design process. You'll learn how we've achieved significant efficiency gains while maintaining the quality of our deliverables, and most importantly, how we maintain the balance between artificial intelligence and human expertise. From research and discovery to design and optimization, we'll show you how LLM tools influence every stage of the conversation design lifecycle.
The AI-Enhanced Conversation Design Workflow
To understand exactly how AI enhances our work, we've been tracking its impact across our projects. The numbers tell a compelling story. Our conversation designers have achieved time reductions ranging from 20% to 90% across various tasks, from content creation to intent development. However, these results don't come from a one-size-fits-all approach.
A key aspect of our AI integration strategy is its flexibility. We don't apply every tool to every project - instead, we match tools to specific project needs and requirements. Our team members also maintain autonomy in choosing which AI tools best complement their workflow. While some designers prefer Wordtune's quick response variations, others might opt for more detailed prompt engineering with ChatGPT or Claude.
Discovery and Research
Every successful conversational AI project starts with thorough research and discovery. This is where our team's strategic use of AI tools first comes into play – not to replace human insight, but to enhance and accelerate our information gathering.
Web Crawling and Information Gathering
Our team employs different AI tools depending on the research needs of each project:
It's worth noting that while AI tools help us gather and analyze public information more efficiently, we never input sensitive client data into these systems. Additionally, when using LLMs for research, our team found that thorough familiarization with materials was still necessary for thorough fact-checking.?
Workshop Data Analysis
The discovery phase often involves stakeholder workshops that generate substantial data. Here's where we've seen more dramatic improvements using Miro AI:
Importantly, we've found that AI excels at initial data organization but requires human oversight to ensure all insights align with project goals and client needs.?
Flow Chart Creation & Dialog Writing
In conversation design, flow chart creation and dialogue/response creation are deeply interconnected. Flow Chart maps out the logic of every step in the assistant’s conversation, ensuring that all paths and possible deviations from paths are accounted for. Response creation centers on crafting the actual words and sentences used by the AI assistant.
Response Variation Generation
While research sets the foundation, the heart of conversation design lies in creating engaging, natural dialogues. This is where our AI toolkit truly shines, showing some of our most impressive efficiency gains. However, each tool comes with its own tradeoffs between speed and customization.
We typically use AI tools after we’ve already written the initial set of AI assistant responses to help us refine tone, improve clarity, adjust formality, or get more response variations.
We’ve found that specialized tools like Wordtune offer the fastest turnaround:
ChatGPT and Claude provide more control but require more setup:
Response Quality Enhancement
We've integrated specialized tools into our workflow for final polish. We use tools like Grammarly and Wordtune for:
While AI tools have been great for improving individual responses, we’ve also found them incredibly helpful for another part of designing conversations: creating intents and managing knowledge bases.
Intent Creation & Knowledge Base Management
Overall, LLMs have helped streamline our intent development and knowledge management processes. However, keep in mind that creating a bot that understands its users well still requires thorough groundwork.
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Intent Development
Intent development entails teaching AI assistants to understand different ways people might ask for the same thing. For example, consider a banking AI assistant that can inform customers about their account balance - there are numerous ways users can ask 'What's my account balance?'. Training phrases are examples we provide to teach the AI assistant how people might say the same thing differently. Using ChatGPT, we've achieved improvements in creating diverse and unique Natural Language Processing (NLP) model training phrases:
Here, it's important to note that AI-generated examples are just a good starting point. Real user training phrases, gathered through testing and actual interactions, remain irreplaceable for training an effective conversational AI system.?
Knowledge Base Processing
A FAQ bot is only as good as its knowledge base (KB). You can think of the knowledge base as the AI assistant’s personal library of answers. Like any library, the KB must be organized and sorted so that the AI assistant can quickly find accurate answers. In the past, organizing this information into clear and easy-to-read knowledge articles was a repetitive and time-consuming task. But thanks to AI, managing knowledge base articles has become faster, smarter, and much less tedious:
As you can see, AI helps us lay strong foundations with efficient intent development and knowledge base organization. However, a bot's initial training is just the beginning. Once it's out in the world, talking to real users, that's when we start learning how well our intents actually cover user needs and whether our knowledge base truly answers their questions.
This is where theme analysis comes in - the process of identifying common topics and patterns in user messages to help us understand what people are really trying to accomplish.?
AI Analysis & Optimization
Our experience with theme analysis offers an important lesson in the limitations of AI tools. When we tried using ChatGPT to identify themes from hundreds of customer utterances, the results weren't as useful as we'd hoped.?
This experience highlighted an important insight: while generalist AI tools like ChatGPT excel at many tasks, specialized solutions often do better at specific jobs. Tools like HumanFirst, for instance, are specifically designed for intent classification and would deliver better results for this particular task.?
Quality Control Practices
Now, how do we make sure all this AI assistance doesn't compromise quality and security? Here’s our approach:
First, we're extremely careful with client information. Sensitive or confidential data never goes into AI tools.
Security Considerations
Verification Process
Additionally, we make sure that all LLM-generated content undergoes a comprehensive human review.
Our AI Tool Stack Summary
To sum up, there isn't one AI solution that fits all conversation design needs. Different stages of our process benefit from different tools. As a result, our team has a diverse AI toolkit.
Summary of results
Our data shows significant time savings:
However, we maintain that efficiency gains should never come at the cost of quality and security.
Key Takeaways
Here's what it all comes down to: we've learned that AI tools can be amazing allies in conversation design when you know how to use them right. Every day, we're discovering new ways to work smarter (not harder) with these tools, while keeping that essential human touch in our conversations.
Our daily experience with these tools has given us unique insight into utilizing AI effectively. We've learned which tools excel at specific tasks and, more importantly, which approaches best serve different business needs.
Ready to start or enhance your conversational AI journey? Let's talk about finding the right solution for your business.