I’m currently going through a tutorial series for LangSmith and just got to the third part, which is about datasets and evaluations. I need help understanding how to create and manage data sets in LangSmith, as I’m a bit confused about the evaluation process.
It seems this section is about prepping datasets for testing language models and conducting evaluations. Yet, I’m unsure about how to best organize my testing data or what the evaluation results mean.
Could someone break down the important concepts I should grasp in this segment of the LangSmith course? I want to ensure I have a solid foundation before progressing. Any useful advice or common mistakes to steer clear of would be greatly appreciated as well.
LangSmith basically automates what you’d do by hand - comparing outputs to expected results. Start with exact match evaluations first, then move to semantic similarity checks. Here’s what surprised me: input quality matters way more than you think. Crappy examples = useless evaluations. Spend time creating realistic inputs that match your real use cases. The evaluation pipeline lets you chain evaluators together, which is great for complex apps. I usually combine correctness and relevance evaluators. Big gotcha: don’t make your expected outputs too rigid or you’ll punish perfectly valid responses that are just worded differently. The trace view saves your life when debugging failed evaluations. You can see exactly what broke and where. Version your datasets as you refine them. Trust me, you’ll forget which changes actually helped otherwise.
Organization is everything with LangSmith datasets. I learned this the hard way - my first evaluation run was completely useless because I mixed different query types together. Each dataset should test one specific capability. Don’t throw customer support and creative writing examples into the same collection. It makes the evaluation metrics meaningless. What really helped was running a baseline evaluation before changing any prompts or model settings. You need something concrete to compare against. When reviewing results, focus on cases where your model was confident but wrong - those usually point to systematic issues, not random errors. LangSmith’s comparison view is incredibly useful for A/B testing different prompt versions. You can run the same dataset against multiple configurations and see exactly where each one succeeds or fails.
the evaluation metrics r confusing at first. start small - grab a few examples and run them manually to see what langsmith’s actually doing. once you understand that, scaling up clicks. don’t stress about perfection right away. u can always refine ur datasets as you figure out what works.
I had the same confusion starting out. Here’s what finally made it click: datasets are just your examples with the right answers attached.
Keep your data simple. One dataset per thing you’re testing. Building a chatbot? Make a “customer_support_queries” dataset with real questions and perfect responses.
Evaluation works like unit testing for AI. Your model gets the input, LangSmith checks what came back against what should’ve happened.
Results give you accuracy scores and flag failures. I always hit the lowest-scoring examples first - they show you where things break.
Biggest mistake I made? Tiny datasets that didn’t match real use. You need 50-100 examples minimum, covering weird edge cases, not just the obvious stuff.
Don’t get fancy with metrics right away. Stick to basic accuracy until you know what matters for your specific case.
The evaluation runner handles batching, so you can test prompt changes fast without checking everything manually.
Been running eval pipelines for years - here’s what actually works: automate everything from data prep to analysis.
Manual dataset management is a total nightmare. You need workflows that pull fresh examples, check data quality, run evals, and spit out reports without you babysitting them.
The game-changer is connecting your eval system directly to your data sources. Pull customer queries from support tickets, grab conversation logs, sync with production metrics. Keeps your test data fresh and actually useful.
Set up alerts when scores tank. Model performance drifts and you want to catch it before it becomes a problem.
Most people completely miss automating the feedback loop. When evals fail, you want systems that flag examples for review, suggest prompt fixes, or retrain based on what they’re seeing.
I’ve built this stuff with traditional tools but the integration work is brutal. You end up spending more time fixing pipelines than actually improving models.
Latenode nails this workflow automation. Connect LangSmith evals to your data sources, set up monitoring, automate the whole feedback cycle. No code needed for the complex orchestration that usually takes weeks.