Even when you integrate the most sophisticated AI into your apps, workflows, or business processes, it is important that AI must respond immediately with the necessary information. This is even more critical in conversational flows since the chat users will instantly stop using interfaces that don’t respond on time. This is where real-time chats powered by the advanced HTTP streaming endpoints come into play.
- Real-Time Data Streaming: HTTP2 and SSE endpoints are available for all conversational and prompt API endpoints for real-time data streaming. This feature is particularly beneficial for AI-driven applications where timely data delivery is crucial and makes a difference for the end user. It allows for immediate transmission of AI responses as they are generated, creating a more dynamic and interactive user experience.
- Typing Effect Simulation: These streaming endpoints can simulate a typing effect in AI-driven chat applications. Streaming AI responses in smaller chunks mimics how humans type messages, making the interaction feel more natural and less robotic. This enhances user engagement as it provides a more conversational and relatable experience. This is similar to how ChatGPT starts sending out the reply right after the moment you ask it something.
- Perceived Speed Improvement: Even though the processing time of AI responses may not change when considering the entire response that needs to be sent, streaming responses in parts (mimicking typing) can make the response times seem faster from a user's perspective. This is because users receive parts of the message as they are "typed out" rather than waiting for the entire response.
- Efficient Use of Resources: HTTP2 specifically, and alternatively, SSE are more efficient with regards to server resource usage than traditional polling methods. They keep a single connection open for streaming data, reducing the overhead and latency of opening new connections for each request.
- Enhanced User Experience: The combination of real-time streaming and the typing effect contributes to a more engaging and interactive user experience. It keeps users engaged during the wait time and adds a layer of realism to AI conversations.
- Auditing and Tracking: Despite the streaming nature of these endpoints, Hyperleap AI maintains precise auditing and tracking for all the AI responses. This ensures that all interactions and data transfers are logged accurately, essential for compliance and improving the AI models based on user interactions.
Hyperleap AI's HTTP2 and SSE endpoints add much value to real-time interactions and user engagement, even as you integrate the best of AI LLMs, all while taking care of auditing and logging each response or gathering feedback from end users. As you integrate Hyperleap AI, you should always consider using these endpoints first, for a seamless user experience especially in cases where the end user is the one interacting with the AI system. In case it’s an app or process instead, normal endpoints can be leveraged, since the code expects the full JSON response before making a business process decision.
