M01·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.
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?
Members onlyJoin 900+ members
Members only from here
This lesson is part of the full AI engineering roadmap. Here's what unlocking gives you.
What you unlock
- 01All 6 modules · 40+ hands-on tutorials
- 02Source code, repositories, and architecture diagrams
- 03Portfolio artifacts: apps, APIs, eval reports, dashboards, capstones
- 04Live sessions + every recording
- 05Discord community access
- 06Certificate of completion with public verification URL
Price·lifetime
$197·Pay once
30-day money-back guarantee. One email, full refund.
or
“Best educational investment in my ML/AI journey.”
Ana Clara Medeiros·AI Developer
Instant access after paymentSecure checkout · stripe