domino-genai-tracing

Name: domino-genai-tracing
Availability: InStock
Author: jvdomino

Trace GenAI executions and evaluate performance within Domino.

Updated Aug 23, 2026

Search Tags:#llm #evaluation #tracing #experiment-tracking #genai #ai-systems #domino

Ready to run

npx skills add https://github.com/jvdomino/domino-data-lab-plugin --skill domino-genai-tracing

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: domino-genai-tracing
Source: https://github.com/jvdomino/domino-data-lab-plugin/tree/main/skills/genai-tracing
Command: npx skills add https://github.com/jvdomino/domino-data-lab-plugin --skill domino-genai-tracing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GenAI tracing in Domino provides end-to-end visibility into LLM calls, agents, and orchestration to support debugging, auditing, and optimization.

Core Features & Use Cases

End-to-end GenAI tracing across LLM calls, agents, RAG pipelines, and multi-step workflows within Domino.
Custom evaluators and MLflow integration to score and aggregate trace metrics for robust experimentation.
Audit-ready traces including token usage, latency, cost, tool usage, and errors to support compliance and analysis.

Quick Start

Run a simple traced workflow inside a DominoRun to observe the captured traces and metrics.

Frequently Asked Questions about domino-genai-tracing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I trace GenAI executions in Domino for auditing and debugging?▼

Trace GenAI executions in Domino by wrapping LLM calls, agents, and RAG pipelines in a DominoRun with the @add_tracing decorator to capture token usage, latency, cost, and errors for audit-ready records.

What metrics are captured when tracing LLM calls and multi-step AI workflows?▼

Tracing LLM calls captures token usage, latency, cost, tool usage, and errors across multi-step AI workflows, providing audit-ready data to support compliance, debugging, and performance optimization.

Does Domino GenAI tracing work with MLflow for experiment tracking?▼

Domino GenAI tracing integrates with MLflow 3.2.0 to score and aggregate trace metrics, enabling robust experiment tracking and custom evaluators for comprehensive performance evaluation.

Can I use custom evaluators to score RAG pipeline performance in Domino?▼

You can use custom evaluators with MLflow integration to score and aggregate trace metrics for RAG pipelines, enabling detailed performance evaluation and robust experimentation within Domino.

What's the best way to get end-to-end visibility into RAG pipelines and agent workflows?▼

Gain end-to-end visibility into RAG pipelines and agent workflows by using the Domino SDK tracing decorators to capture comprehensive execution data including token counts, tool usage, and errors.

Do I need the Domino SDK to trace GenAI applications?▼

You need the Domino SDK to trace GenAI applications, using the @add_tracing decorator and DominoRun class to capture execution metrics and integrate with MLflow for evaluation.