Hey everyone! 👋 So I've just finished the official LangSmith quickstart, got my first trace showing up in the dashboard, and... now I'm staring at the UI feeling a bit lost. The quickstart was great for the "hello world" of tracing, but it feels like I've been given the keys to a spaceship cockpit without a manual. There are so many menus—Projects, Datasets, Evaluations, Annotations—and I'm not sure what to tackle next for a simple but realistic project.
My immediate goal is to improve a small, experimental chatbot I built with LangChain, but I'm not clear on the *practical* workflow. The docs explain each feature in isolation, but I'm missing the connective tissue. For instance:
* Should I immediately start logging all my LLM calls to a specific Project, or is that overkill for just testing?
* What's the first, most impactful thing I should set up after basic tracing? Is it automatic evaluation, creating a dataset for testing, or something else entirely?
* I see "Annotations" and "Feedback"—what's the actual difference in daily use?
I'd love to hear from anyone who's been through this initial "now what?" phase. What was your first real project workflow with LangSmith? Maybe something like:
1. **Extended Tracing**: First, I modified my chain to add more metadata (like user IDs) to the runs by using the `LangSmithTracer` more explicitly.
```python
from langsmith import Client
client = Client()
# ... then configured my chain with callbacks for more detail
```
2. **Dataset Creation**: Then, I took a handful of real user queries from my logs and made a simple CSV dataset to test against.
3. **Basic Evaluation**: I started with a simple custom evaluator to check response length, before diving into AI-assisted scoring.
What order would you recommend? Are there any "gotchas" in setting up a project that you wish you'd known? Also, for those using VS Code, have you found any useful extensions or tricks for working with the LangSmith API locally? Any tips for structuring code to make it easier to integrate tracing from the start?
Basically, I need the "chapter two" the quickstart doesn't provide. The view from the first step on the ladder. Any stories, workflows, or even config snippets would be hugely appreciated!
editor is my home
Totally get that cockpit feeling, I was there just a few weeks ago! What finally clicked for me was ignoring most of the UI at first and focusing on one thing: turning my experimental chatbot calls into a proper project. It's not overkill, it actually simplifies things because everything gets organized in one place automatically.
For your next step, I'd honestly skip evaluations and datasets for a moment. Just hook your chatbot up to log to a project, then have a few real conversations with it. After that, go look at those traces and use the "Add Annotation" button to manually score a few responses as good or bad. That manual feedback loop was the biggest lightbulb moment for me - it showed me exactly which prompts were drifting off topic before I even thought about automated tests.
By the way, the difference between Annotations and Feedback still trips me up sometimes. From what I gather, annotations are your team's labels for analysis, and feedback is more for end-user ratings, like a thumbs-up/down in an app. Have you found a good way to simulate user feedback yet, or are you just winging it with test conversations?