Retrieval-Augmented Generation (RAG) for Intent Recognition

We're excited to announce a significant upgrade to our intent recognition system, moving from the traditional Natural Language Understanding (NLU) approach to Retrieval-Augmented Generation (RAG) model using embeddings. This transition brings notable improvements to the speed, accuracy, and overall user experience when interacting with AI agents on our platform.

📅 Phased Rollout

To ensure a smooth adoption, we will be rolling out the RAG-based intent recognition system to all users in phases over the next week. This gradual deployment allows us to monitor performance and gather feedback while providing ample time for you to adjust to the new system.

🆕 Default for New Projects

For all new projects created on our platform, the RAG-based intent recognition will be the default system. This means that new AI agents will automatically benefit from the enhanced speed, accuracy, and natural conversation capabilities offered by RAG.

🌟 Faster Training and Interaction

With the new RAG system, agent training and intent recognition are now substantially faster and more efficient. For example, an agent with 37 intents and 305 utterances now trains about 20 times faster, in just around 1 second. This means quicker agent development and smoother conversations for end-users.

🧠 Automatic Agent Training

Thanks to the advanced training speed enabled by RAG, explicit training is no longer necessary. Simply test your agent, and the training will happen automatically behind the scenes, streamlining your workflow.

🎯 Enhanced Understanding of Complex Queries

RAG leverages embeddings to capture the deeper context and meaning behind words, even when phrased differently. This allows the system to better understand and accurately match complex, detailed questions to the appropriate intents, providing more precise responses to users.

🗣️ More Natural Conversations

With the improved understanding of casual language, slang, and diverse phrasing, the RAG system enables a more natural, conversational experience for users interacting with AI agents on our platform.

🔄 Seamless Transition for Existing Projects

For existing projects, we will keep both the NLU and RAG systems running concurrently for a period of time. This allows you to explore the new system, test it thoroughly, and make any necessary adjustments to your agents. You can easily switch between the NLU and RAG systems in the intent classification settings within the Intents CMS.

We're thrilled to bring you this enhanced experience and look forward to hearing your feedback as you interact with the new RAG-based intent recognition system. Your input is invaluable in helping us continue to innovate and improve our platform to better serve your needs.