Technical approach for classifying human-AI interactions at scale

TL;DR

• Semantic Telemetry helps LLMs run efficiently, reliably, and in near real-time.
• The post explains the engineering behind that system, including trade-offs and lessons learned.
• It covers batching strategies, token optimization, and orchestration.
• It frames the work within the broader challenge of understanding how users interact with AI systems.

Context and background

Semantic Telemetry is described as a mechanism to understand how users interact with AI systems. In the Microsoft Research blog, the goal is to share the engineering behind such a system and how teams can apply these lessons when classifying human-AI interactions at scale. As described in the Microsoft Research blog Technical approach for classifying human-AI interactions at scale. The post highlights the importance of scalable, near real-time insights into user interactions to support reliable LLM-driven services. By examining how people engage with AI systems at scale, the work aims to guide the design and operation of production AI platforms.

What’s new

The article presents a technical approach for classifying human-AI interactions at scale, outlining the methods used to capture, categorize, and analyze interactions as they occur in real time. It emphasizes the trade-offs involved and the lessons learned from building scalable telemetry systems. The piece places particular focus on batching strategies, token optimization, and orchestration as central topics in the architecture.

Why it matters (impact for developers/enterprises)

For developers and enterprises, a scalable approach to classifying interactions informs design choices, monitoring, and optimization of AI services. The post argues that gaining structured insight into how users engage with models helps improve reliability and efficiency, and it provides a framework for evaluating interaction data at scale.

Technical details or Implementation

Batching strategies

The post discusses batching strategies as part of the Semantic Telemetry system, noting their role in handling multiple interactions efficiently within the processing pipeline. It describes that batching is part of the engineering approach to classifying human-AI interactions at scale.

Token optimization

Token optimization is presented as a key area of focus within the system, with the post outlining how tokens are managed and optimized as part of the classification and telemetry workflow.

Orchestration

Orchestration is covered as another critical component, detailing how the different parts of the telemetry and classification pipeline are coordinated to operate at scale.

Key takeaways

• Semantic Telemetry provides a practical means to understand user interactions with AI systems.
• The engineering behind the telemetry system is central to running LLMs at scale.
• The post highlights batching strategies, token optimization, and orchestration as core elements.
• It emphasizes the importance of trade-offs and lessons learned in building scalable telemetry.
• The approach supports informed design, monitoring, and optimization for enterprise AI deployments.
• Readers can refer to the original Microsoft Research article for deeper technical detail.

References

• Microsoft Research blog: https://www.microsoft.com/en-us/research/blog/technical-approach-for-classifying-human-ai-interactions-at-scale