Hey everyone. Just started using LangSmith for a project and I keep mixing up 'Projects' and 'Datasets' in my head. 😅
They both seem like containers for organizing your work. Can someone explain the main difference in a simple, practical way? Like, when do I create a new Dataset vs. a new Project? Maybe an example from a basic chatbot setup would really help.
Totally get the confusion. I thought of it this way after bumping into the same wall.
A Project is like a folder for a specific version of your application. You're tracking all the runs (tests, debugging, actual usage) for that specific chain or agent. If you build a new version of your chatbot with a different prompt or logic, that might be a new project.
A Dataset is a standalone test set you can use *across* projects. It's your benchmark questions and expected outputs for your chatbot. You'd evaluate your current "Project" against that "Dataset" to see if changes improved things.
So for a basic chatbot:
- You create a Dataset called "customer_service_benchmark" with 100 example questions.
- You build your first chatbot, "helpbot_v1". That's a Project.
- You run evaluations, sending the dataset questions through your project.
- You tweak the prompt, and create a new Project, "helpbot_v2".
- You evaluate *that* project using the same original "customer_service_benchmark" dataset to compare results.
Benchmarks or bust
That's the right mental model. I'd add a real cost wrinkle: you're not just evaluating with that dataset, you're paying for each evaluation run. If you're not careful, copying a huge dataset into multiple projects just to run the same tests can quietly double your bill.
Your example shows the strength, but the billing treats them as separate executions. Keep one golden dataset and reference it, don't duplicate it for each new project version.
Cloud costs are not destiny.
Finally, someone bringing up the actual pricing gotchas. It's not just a double-billing risk if you copy datasets.
The real fun starts when you realize you can't easily *freeze* a dataset version. Say you tweak your "golden" benchmark to add a few more edge cases. Now, your evaluation of old Project A (which used v1 of the dataset) isn't directly comparable to Project B (which uses v1.1). The historical comparisons get fuzzy, but the billing stays crystal clear. So much for a clean benchmark over time.
Trust but verify.
That's a really good point about freezing versions. It reminds me of tagging releases in GitHub. If a dataset is meant to be a benchmark, shouldn't it have immutable snapshots?
How do teams usually handle this, just accept the drift? Or do you have to create a whole new duplicate dataset with a "v2" name every time you need to add a test case, just to keep the old one untouched for comparison? That seems like it would create clutter fast.