Loading...
Loading...
Your agent shipped, your traces look fine, and someone in standup just asked if it is actually good. You do not have an answer. This course gives you one. Build the eval datasets, the LLM-as-judge graders, the trajectory checks, and the production feedback loops that turn "looks alright" into a number you can defend.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Score AI agents the way teams who ship them actually do. Build eval datasets in LangSmith, grade answers and reasoning trajectories, run RAGAS on retrieval, and wire automated feedback into production traces. A guided curriculum with hands-on labs that turns "looks alright" into a number you can defend.
Stop shipping agents you cannot defend. Learn the eval patterns LangSmith-backed teams actually use.
What you'll ship
What you'll learn
Curriculum
Foundations and dataset thinking
Why agent eval breaks every assumption from unit testing, how LangSmith reframes the problem, and how to build the first dataset that makes everything downstream possible.
Score the answer
Pick the right grader for the output shape. Exact match for facts, LLM-as-judge for prose, structured data evaluators for JSON. Stop misranking your agent because the grader did not fit the answer.
Score the reasoning
A right answer can come from broken reasoning. Catch silent regressions with dynamic ground truth, trajectory evaluation, and tool-precision metrics that score the path the agent took.
Score the retrieval
Retrieval is where most RAG agents quietly break. Isolate the generator with component-wise eval, run the RAGAS scorecard end-to-end, and use pairwise comparison for the close-call decisions aggregate scores miss.
Run evals in production
Move beyond offline scoring. Attach real-time evaluators as callbacks, benchmark conversational agents with simulated users, and run scheduled batch jobs that enrich every production trace with quality scores.
Who it's for
Your agent works in demos and breaks in subtle ways for real users. You need a measurement layer that catches regressions before customers do.
You changed the system prompt. Did it actually help? Pairwise comparison and trajectory eval give you an answer, gut feel does not.
Multiple teams need a consistent way to grade their agents. You want a harness, dataset patterns, and CI integration you can hand to other teams.
You need a credible answer when leadership asks if the agent is improving. Continuous LangSmith dashboards and pairwise reports give you one.
FAQ
Advanced RAG Evaluation focuses on the retrieval pipeline using RAGAS and component-wise scoring. This course covers the full agent surface, including answer scoring, trajectory and tool-precision evaluation, simulated user benchmarks, and production feedback loops. The two complement each other, and you should take both if you ship retrieval-heavy agents.
You can follow with the example agent from the source repo, but you will get the most value if you bring your own agent code. The lessons explicitly show how to wire each technique into existing LangChain, LangGraph, or custom-loop agents.
Yes for the LangSmith-specific patterns, which is most of the course. The dataset, evaluator, and feedback APIs come from LangSmith. The free LangSmith tier is enough for the labs, and the patterns translate to other observability platforms (Phoenix, Langfuse) with adapters covered in the closing module.
Examples use OpenAI and Anthropic models. Any chat-completion provider works with minor swaps. The eval patterns themselves are model-agnostic.
Pricing
One subscription unlocks every paid course and workshop replay. Pick yearly or monthly.
Unlock with Pro
You save 47% with regional pricing
Billed annually. Cancel anytime.
Still deciding? Ask Param a question
Engineers who can defend their agent in a postmortem keep their agents in production. The rest get rewritten by the next team.
Agent evaluation techniques
From $16/mo with Pro