? ACCESS THE FREE HANDS-ON LABS HERE : https://kode.wiki/4dpIxvM
Want to build LLM applications and AI agents that you can actually trust in production? In this complete LangSmith tutorial, you'll learn how to evaluate, test, and monitor LLM systems the way professional AI teams do.
We dive deep into LLM evaluations — the most overlooked yet most important part of building reliable AI applications. From tracing LLM calls and building golden datasets to creating custom evaluators, LLM-as-a-Judge, and full production readiness reports — this course covers it all with hands-on labs you can practice for FREE.
? What You'll Learn:
✅ What LLM evaluations are and why they matter
✅ How to integrate LangSmith with your AI applications
✅ Tracing LLM calls (latency, tokens, cost)
✅ Building golden datasets with edge cases & adversarial inputs
✅ Exact Match, Contains, Keyword Coverage & LLM-as-a-Judge evaluators
✅ Running and comparing experiments
✅ Offline vs Online evaluations
✅ Building a Production Readiness Report
⏰Timestamps:
00:00 - Introduction to LangSmith Tutorial
01:31 - What Are LLM Evaluations?
06:47 - How LangSmith Solves Evaluation Problems
09:44 - Why your Agents can still fail?
10:29 - What You Can Measure in LangSmith
11:02 - The LangSmith Evaluation Workflow
13:47 - DEMO 1 - LangSmith Dashboard Walkthrough
17:31 - LAB 1 - Evaluating LLM systems with LangSmith
28:43 - What makes a Good Evaluation Dataset
32:38 - DEMO 2 - Creating a JSON Dataset
34:52 - LAB 2 -Building a Golden Dataset
43:22 - Core Dimensions and Metrics of LLM Evaluations
48:34 - DEMO 3 - Creating Evaluators in Code
50:23 - LAB 3 - Complete Evaluation Pipeline
59:32 - Evaluators & Metrics in LangSmith
01:04:22 - DEMO 4 - Using LangSmith Evaluation Python Client
? Includes 3 hands-on labs with step-by-step walkthroughs!
? Access the free labs using this link - https://kode.wiki/4dpIxvM
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