# Day 2 Lecture: Understanding LLM Reasoning traceability

> Understanding LLM reasoning traceability is crucial for evaluating and improving AI applications. This session covers the importance of tracing and evaluating large language models (LLMs), focusing on techniques for assessing their performance in production. Participants will learn about tools like…

- Web page: https://www.dataexpert.io/lesson/ml-ops-day2-lecture-july25
- Program: [AI Engineering Boot Camp Starting on July 7th, 2025](https://www.dataexpert.io/program/ai-boot-camp-starting-june-7th-8623)
- Access: Requires enrollment in AI Engineering Boot Camp Starting on July 7th, 2025
- Length: 34 min video
- Skills: NLP, LLMs, Problem Solving
- Academy: DataExpert.io Academy

## About this lesson

Understanding LLM reasoning traceability is crucial for evaluating and improving AI applications. This session covers the importance of tracing and evaluating large language models (LLMs), focusing on techniques for assessing their performance in production. Participants will learn about tools like Lang Smith for evaluation, the significance of guardrails, and production challenges such as accuracy and latency, ensuring they can effectively deploy and monitor AI 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.md)
- Next lesson: [Day 2 Lab: Tracing LLM reasoning with LangChain + LangSmith](https://www.dataexpert.io/lesson/ml-ops-day2-lab-july25.md)