Microsoft’s Semantic Telemetry: Scaling Human-AI Conversation Classification for the Future

As artificial intelligence weaves its way deeper into mainstream society, the need to understand, categorize, and optimize human-AI interactions has moved from the realm of theoretical importance to practical necessity. Nowhere is this more apparent than in conversational agents powered by large language models (LLMs), where millions—sometimes billions—of conversations occur every day. For Microsoft and other leading tech giants, orchestrating, analyzing, and learning from this data is not just an exercise in analytics, but a mission-critical challenge that touches product design, user safety, and the overall direction of AI research.

The Rise of LLMs and the Need for Scalable Classification​

Over the past several years, large language models have become fundamental building blocks for AI systems. Their introduction has facilitated more natural, flexible engagement between users and machines, allowing for richer, more responsive dialogue systems. However, with this advancement comes a new scalability hurdle: each message exchanged, each query posed and answered, generates valuable (and complicated) signals about user satisfaction, expertise, intentions, and much more.
Traditional telemetry methods—pageviews, clicks, superficial engagement metrics—fall short in extracting nuanced insights from these conversations. What’s needed is a mechanism for semantic telemetry: a system that can detect, at scale, what’s actually happening inside these user-agent exchanges: Who is asking? What is their domain knowledge? How satisfied are they with the answers? What topics prevail, and which are underserved?

Enter Semantic Telemetry: Microsoft’s Approach​

Microsoft’s response to this challenge is embodied in the Semantic Telemetry project, a sophisticated LLM-powered pipeline that classifies hundreds of millions of anonymized Bing Chat conversations per week. This system provides continuous, near-real-time feedback about how users interact with their AI—informing not just product iteration but also foundational AI research.
The end goals are straightforward:

• Drive ongoing refinement of AI systems based on real user behavior.
• Enable rapid identification of emerging topics and patterns.
• Provide granular insight into user satisfaction, engagement, and expertise, thereby directly influencing product evolution and reliability.

But while these objectives are conceptually simple, the engineering behind making them reality is anything but.

Engineering for Scalability: Architectural Highlights​

At its core, the Semantic Telemetry pipeline is a highly-scalable, modular ETL (Extract, Transform, Load) system—augmented by innovative architectural choices that make it uniquely adept at LLM integration. Let’s break down the key components and the rationale behind them.

Hybrid Compute Engine: PySpark Meets Polars​

The foundation of the pipeline is a hybrid compute engine combining two technologies:

• PySpark gives the system distributed muscle, enabling seamless scaling across massive datasets with existing big data infrastructure.
• Polars, a lightning-fast DataFrame library, powers lightweight execution in environments where running Spark clusters would be overkill.

This duality means that the same codebase can process petabytes on a cluster or crunch through test datasets on a developer’s laptop without modification—maximizing flexibility and maintainability.

LLM-Centric Transformation Layer​

Unlike standard ETL pipelines that might stick to basic data cleaning, Semantic Telemetry incorporates a robust LLM-driven transformation phase. Here’s how:

• Model Agnosticism: The transformation layer supports multiple LLMs and adapts to new models with minimal changes.
• Prompt Templates: Built on the Prompty language specification, templates enforce consistency, encourage prompt reuse, and allow for easy customization.
• Output Normalization: Automated logic parses and cleans model responses, correcting format issues, standardizing labels, and dealing with errors like near-matches (“create” vs. “created”) or invalid outputs.

Crucially, this layer is modular, separating the concerns of prompt definition, model invocation, and result parsing. As a result, new classifiers, prompt styles, or models can be rolled out with minimal risk and effort.

Modular Multi-Task Support​

One of the greatest strengths of the pipeline is its ability to run multiple, diverse classification tasks—such as user expertise estimation, topic detection, and satisfaction analysis—over the same set of conversations. This flexibility means that product teams and researchers can adapt the pipeline for new measurement goals or experiment with fresh classification strategies without re-architecting the entire system.

The Real-World Hurdles of LLM-Oriented Engineering​

Even the most elegant architecture must contend with the unpredictable realities of running LLMs in production, especially at Microsoft’s scale. Several recurring themes define the engineering challenge—and the innovative responses that emerged.

Latency and Endpoint Unpredictability​

LLM endpoints (especially those accessed remotely, like Azure OpenAI) often introduce variable response times due to model load, network traffic, and other factors. For a pipeline processing hundreds of millions of requests per week, even minor delays compound into significant bottlenecks.
Microsoft’s Solution:

• Rotating across multiple endpoints to balance load and bypass slow nodes.
• Storing intermediate results asynchronously to protect against transient failures.
• Preferring models with high tokens-per-minute (TPM) ceilings, like GPT-4o mini (which boasts a 2M TPM rate—25x that of GPT-4).
• Implementing robust timeout and exponential backoff mechanisms to shield the system from cascading failures.

These choices reflect a clear understanding: reliability cannot depend solely on endpoint performance; the pipeline must be proactive in managing variability and mitigating risk.

Rapid Model Evolution and Prompt Alignment​

New LLMs arrive at a dizzying pace, each bringing subtle shifts in output style or performance. These changes can break prompt alignment, scramble output formatting, or alter classification logic, undermining the reliability of downstream analytics.
Microsoft’s Solution:

• Routine small-sample A/B tests pit new models against baselines, with distributional comparisons guiding rollout decisions.
• For misaligned models, iterative prompt retuning helps restore consistency.
• Tools like Sammo provide side-by-side comparisons of model outputs, enabling detailed impact analysis.

Here, the focus is on rigorous, empirical evaluation—a practice necessary both for maintaining long-term data consistency and for leveraging genuine model improvements when they arise.

Dynamic Concurrency: The Art of Parallelization​

With rate limits and unpredictable latency plaguing LLM endpoints, pipeline throughput depends heavily on effective concurrency management. Too little parallelism leaves compute on the table; too much causes throttling and failures.
Microsoft’s Solution:

• The system continuously gauges the number of active tasks (e.g., Spark executors, async workers) to set an optimal concurrency baseline.
• Success and failure rates are tracked in rolling windows. Spikes in failure trigger backoff, while sustained success leads to gradual ramp-up.
• Latency feedback loops allow the system to scale back before visible rate-limit errors occur, ensuring responsiveness and stability.

This approach is as much an art as a science—requiring fine-tuned heuristics and constant monitoring to avoid both underutilization and overcommitment.

Optimization Experiments: Strategies That Move the Needle​

Given the scale and complexity, incremental efficiency gains translate into massive savings—both computationally and in real dollars. Microsoft trialed several innovative approaches:

Batch Endpoints​

Batch endpoints allow large numbers of LLM prompts to be processed over extended periods (typically 24 hours), delivering marked throughput increases and cost savings (around 50% cheaper than non-batch). However, they’re ill-suited for use cases requiring real-time feedback, as they introduce significant delays.

Conversation Batching in Prompts​

Processing multiple conversations in a single LLM call drastically reduces token consumption and speeds up throughput. However, this introduces the risk of classification “interference,” where answers can bleed across contexts, affecting accuracy. Microsoft mitigated this with “grader LLM prompts”: after initial classification, another LLM is asked to identify misclassified conversations for targeted reprocessing. Still, repeated experiments showed up to a 20% swing in assigned domains, signifying caution is warranted.

Multi-Task Prompts​

Combining several classifiers (for example, topic and satisfaction) in a single prompt increases efficiency by reducing duplicate transmission of conversation text and multiplying throughput. However, this introduces accuracy trade-offs, as models may confuse subtasks or degrade under heavy cognitive load.

Text Embedding Approaches​

Rather than invoking an LLM for every classifier and conversation, Microsoft explored training custom neural models on conversation embeddings generated by LLMs. The advantages are considerable: each conversation is only passed once to generate embeddings, and all classifier models share that data. While throughput surges and costs plummet, some accuracy is sacrificed, and expanded GPU requirements complicate infrastructure. For research environments or non-critical measurement, this approach can be a game-changer.

Prompt Compression​

Removing unnecessary tokens (either by hand or via automated tools like LLMLingua) slashes request size, trims costs, and lifts throughput by increasing the number of requests possible before hitting TPM limits. However, excessive compression may obscure key context or information, reducing classification quality. The process itself can introduce overhead if not carefully implemented.

Text Truncation​

Cutting conversations to a set length also brings efficiency gains—less text equals lower cost and higher throughput. But judgments around where to slice, and how short to go, are highly delicate: important conversational context can be lost, degrading classifier accuracy (especially on complex, open-ended interactions).

Critical Analysis: Balancing Strengths and Risks​

Evaluating Microsoft’s Semantic Telemetry approach brings into focus both the strengths of their technical strategy and some looming challenges inherent to this domain.

Notable Strengths​

• Architectural Flexibility: The hybrid PySpark/Polars setup, along with modular transformation stages, ensures adaptability across environments and compute budgets.
• Empirical Rigor: Systematic, large-scale A/B testing of model releases and prompt alignments verifies that deployment decisions are data-driven, not speculative.
• Real-World Focus: Every design choice reflects hands-on learning from live production loads, not just theoretical “best practices.”
• Cost and Throughput Awareness: Careful experimentation with batching, compression, and embedding demonstrates a clear focus on value—not just raw accuracy or speed.
• Continuous Monitoring: Feedback loops in concurrency control, as well as responsive pipeline adjustments, embody modern DevOps and MLOps best practices.

Potential Risks and Gaps​

• Classification Accuracy Trade-Offs: Any optimization—be it batching, truncation, or embedding—risks degrading classification quality. As evidenced by Microsoft’s experiments, swings of up to 20% in some tasks are possible, necessitating constant vigilance and advanced error analysis.
• Model Drift: As LLMs rapidly evolve, maintaining prompt alignment and result consistency is a moving target. Even with robust monitoring, subtle shifts may only become apparent after significant data volatility.
• Endpoint Reliability: The pipeline’s reliance on remote LLM endpoints means network issues, model downtime, or provider-side policy changes can cause systemic disruptions.
• Cost Predictability: While efficiency strategies lower the average price tag, peak demand periods can still cause significant cost fluctuations—especially when fallback strategies trigger increased on-demand endpoint usage.
• Privacy and Security: Though the system works with anonymized data, processing hundreds of millions of real user conversations brings ongoing privacy risk. Clear protocols and strong oversight are essential to remain trustworthy and compliant.

The Future of Semantic Telemetry and Classifier Systems​

As artificial intelligence continues its rapid expansion, scalable, high-throughput classifier pipelines like Microsoft’s Semantic Telemetry will become the backbone of safe, effective, and adaptable AI deployment. Every major player in the conversational AI space—Google, OpenAI, Anthropic, Amazon—is wrestling with similar trade-offs and scalability challenges. Microsoft’s public exploration of both their successes and pitfalls is an industry-leading example of transparency and technical rigor.
Looking forward, several themes will likely dominate future developments in this space:

• Greater Model Versatility: Expect pipelines to become even more agnostic, tapping into best-in-class LLMs from a multitude of vendors, or even mixing closed and open-source models.
• Finer-Grained Controls: Emerging techniques like selective prompt augmentation, transformer-based prompt engineering, and active learning could further balance throughput and accuracy.
• Edge Processing: With advances in lightweight models, some classification will move closer to the data source, improving privacy and lowering latency.
• End-to-End Automation: As orchestration and monitoring advance, human intervention in prompt alignment and model selection will decrease, empowering even greater scale and rapid experimentation.

Conclusion​

Building an efficient, reliable, and adaptive classification pipeline for human-AI conversations is a daunting technical challenge, but one that pays significant dividends in product quality, user trust, and research insight. Microsoft’s Semantic Telemetry project stands as a leading example of how scaling LLMs to millions of user interactions is not simply a matter of adding compute—it requires architectural finesse, relentless monitoring, and a willingness to experiment and learn from failure.
While the path is still marked by trade-offs—in accuracy, cost, and reliability—the evolving strategies and lessons shared by Microsoft provide a robust blueprint for organizations seeking to unlock the true value of human-AI interaction data. As the industry continues to push the boundaries of what conversational AI can do, scalable, transparent, and continually-improving systems like Semantic Telemetry will be essential in shaping both the technology and the discourse around responsible AI at scale.

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Source: Microsoft A technical approach for classifying human-AI interactions at scale