This article explores how OpenTelemetry Semantic Conventions for Generative AI provide deep visibility into LLM-powered applications by standardizing the recording of model calls, tool invocations, and token exchanges. It provides a practical walkthrough on exporting telemetry from tools like VS Code Copilot and using the Aspire Dashboard to visualize traces, metrics, and chat-style conversations.
"Prove AI is a self-hosted solution designed to accelerate GenAI performance monitoring. It allows AI engineers to capture, customize, and monitor GenAI metrics on their own terms, without vendor lock-in. Built on OpenTelemetry, Prove AI connects to existing OpenTelemetry pipelines and surfaces meaningful metrics quickly.
Key features include a unified web-based interface for consolidating performance metrics like token throughput, latency distributions, and service health. It enables faster debugging, improved time-to-metric, and better measurement of GenAI ROI. The platform is open-source, free to deploy, and offers full control over telemetry data."
Key features include a unified web-based interface for consolidating performance metrics like token throughput, latency distributions, and service health. It enables faster debugging, improved time-to-metric, and better measurement of GenAI ROI. The platform is open-source, free to deploy, and offers full control over telemetry data."
Logs, metrics, and traces aren't enough. AI apps require visibility into prompts and completions to track everything from security risks to hallucinations.
Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
A discussion post on Reddit's LocalLLaMA subreddit about logging the output of running models and monitoring performance, specifically for debugging errors, warnings, and performance analysis. The post also mentions the need for flags to output logs as flat files, GPU metrics (GPU utilization, RAM usage, TensorCore usage, etc.) for troubleshooting and analytics.
Langfuse is an open-source LLM engineering platform that offers tracing, prompt management, evaluation, datasets, metrics, and playground for debugging and improving LLM applications. It is backed by several renowned companies and has won multiple awards. Langfuse is built with security in mind, with SOC 2 Type II and ISO 27001 certifications and GDPR compliance.
Why evaluating LLM apps matters and how to get started
