# Day 2 Lecture: Understanding LLM Reasoning traceability

> Understanding LLM reasoning traceability is essential for evaluating and optimizing AI applications in production. This lesson covers key concepts such as tracing, evaluation techniques, and the importance of guardrails when deploying models. Participants will learn how to assess model performance,…

- Web page: https://www.dataexpert.io/lesson/ml-ops-day2-lecture-july25-p737-l1186
- Program: [AIExpert](https://www.dataexpert.io/program/ai-expert)
- Module: Week 4: MLOps, Guardrails & Business Value
- Access: Requires enrollment in AIExpert
- Length: 34 min video
- Skills: NLP, LLMs, Problem Solving
- Academy: DataExpert.io Academy

## About this lesson

Understanding LLM reasoning traceability is essential for evaluating and optimizing AI applications in production. This lesson covers key concepts such as tracing, evaluation techniques, and the importance of guardrails when deploying models. Participants will learn how to assess model performance, address production challenges, and utilize tools like Lang Smith for effective monitoring and evaluation, benefiting data engineers and AI practitioners looking to enhance their applications.

## Navigation

- Previous lesson: [Day 1 Lab: Setting up CI/CD for an AI application](https://www.dataexpert.io/lesson/ml-ops-day1-lab-july25-p737-l1184.md)
- Next lesson: [Day 2 Lab: Tracing LLM reasoning with LangChain + LangSmith](https://www.dataexpert.io/lesson/ml-ops-day2-lab-july25-p737-l1185.md)