Observability and Tracing

When your RAG pipeline returns a wrong answer, was it bad retrieval or LLM hallucination? Tracing shows you exactly which step broke and why. Learn how to trace your RAG pipelines with MLflow.

When your RAG pipeline starts returning wrong answers you have no way to tell whether retrieval pulled the wrong documents or the LLM hallucinated over perfectly good context. Tracing allows you to record a per-request execution timeline (including inputs, outputs, and duration), which allows you to pinpoint failures without staring at the logs for hours.

What You'll Build

Run MLflow locally via Docker Compose
Automatically trace LangGraph workflows
Add manual spans to retrieval and generation functions
Attach custom attributes to traces
Query and analyze traces programmatically

Why Trace?

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“Best educational investment in my ML/AI journey.”

Ana Clara MedeirosAI Developer

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What you get

6 modules and 42 hands-on lessons with runnable code, failure diagnosis, and measured comparisons

2 capstone repositories you build, test, evaluate, and keep as portfolio proof

Videos, source code, complete projects and architecture diagrams

Recordings of live sessions

Discord community access

Not ready? Try these free lessons first

LangGraph QuickstartRAG and Context Engineering

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Footnotes

mlflow-tracing PyPI package ↩

RAG and Context Engineering

Observability and Tracing

When your RAG pipeline returns a wrong answer, was it bad retrieval or LLM hallucination? Tracing shows you exactly which step broke and why. Learn how to trace your RAG pipelines with MLflow.