I’m trying to figure out how to perform an evaluation through the LangSmith user interface but I’m having trouble finding the right steps. I’ve been working with LangSmith for a while now and I can handle basic tasks, but when it comes to running evaluations directly from the UI, I get confused about where to start. Can someone walk me through the process? I need to know which buttons to click, what settings to configure, and if there are any prerequisites I should be aware of before starting an evaluation. Any tips on common mistakes to avoid would also be helpful since I don’t want to mess up my data or waste time on incorrect configurations.
The Problem:
You’re having trouble performing evaluations through the LangSmith user interface, specifically understanding the necessary steps, button clicks, settings, and prerequisites. You’re unsure about how to configure settings and want to avoid common mistakes to save time and prevent data corruption.
Understanding the “Why” (The Root Cause):
LangSmith’s evaluation interface requires a specific workflow and understanding of its components. The core process involves comparing your model’s outputs against expected results from a dataset. Errors often arise from mismatched data formats between your dataset and the evaluation metrics, incorrect input/output mappings, or overlooking the importance of schema validation. Starting with smaller datasets helps to debug issues before scaling up to larger evaluations, preventing wasted time and computational resources.
Step-by-Step Guide:
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Prepare Your Project: Ensure you have a dataset uploaded and some logged runs from your application within your LangSmith project. Without these, the evaluation options won’t be available. Carefully review your dataset’s column names to ensure they align with your model’s expected inputs.
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Access the Evaluation Creation Wizard: Navigate to your project dashboard. In the left sidebar, locate ‘Evaluations’ and click ‘New Evaluation.’ This will open the evaluation creation form.
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Configure Your Evaluation: This form has three main sections:
- Dataset Selection: Choose the dataset you prepared in step 1.
- Run Selection: Select the runs or traces you want to evaluate.
- Evaluator Setup: This is where you choose the metrics to use. Begin with built-in metrics like accuracy or relevance before moving to more complex custom evaluators. Use the preview feature to verify that your dataset fields correctly map to your runs’ input and output data.
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Run the Evaluation: Start with a small subset of your data to test the evaluation. Larger datasets can take a considerable amount of time and processing power; testing a small subset helps with debugging and avoids consuming excessive quotas. For larger datasets, set the evaluation to run asynchronously to prevent browser timeouts.
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Review the Results: Once your evaluation is complete (indicated by a status change), the results will appear in the ‘Evaluations’ tab. LangSmith provides a clean interface for comparing different runs side by side. You can also filter and export the data for further analysis.
Common Pitfalls & What to Check Next:
- Data Mapping: Pay close attention to the data mapping between your dataset columns and your model’s input/output. Mismatched data types can cause silent failures.
- Schema Validation: The schema validation step is crucial. Ensure your dataset format adheres to what the evaluators expect.
- API Limits: Be mindful of your API rate limits. Large evaluations consume significant resources; avoid exceeding limits to prevent interruptions. Start with small evaluations.
- Asynchronous Processing: For large datasets, always use asynchronous evaluation to prevent browser timeouts and data loss.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
LangSmith’s evaluation interface has a specific order you need to follow. You can’t jump straight to evaluations - set up your project first. Go to your target project and make sure you’ve got datasets and traced runs ready. Find the evaluation creation wizard in the main nav under Evaluations. The data mapping stage is where things get tricky. The interface shows how your dataset fields line up with your runs, but mismatched field types fail silently and they’re a pain to debug. Start small - test with tiny dataset chunks first. Small batches process right away, but bigger ones run in the background. Watch the status indicator to see where you’re at. Once it’s done, you’ll see results in the same workspace with filters and comparison tools. You can export everything if you need it for other analysis work.
The evaluation workflow makes way more sense once you break it down step by step. Go to your project and make sure you’ve got a dataset uploaded plus some logged runs from your app. Here’s what clicked for me: evaluations are just comparing your model’s outputs against what you expected.
Hit Evaluations from the project view, then Create Evaluation. You’ll see three sections - dataset selection, run selection, and evaluator setup. Watch out for the schema validation step. This totally caught me off guard when my dataset format didn’t match what the evaluators wanted.
I learned the hard way that batch evaluation beats evaluating individual runs every time. For bigger datasets, set it to run asynchronously or your browser will timeout and you’ll lose everything.
The results dashboard breaks everything down nicely - you get per-example scores and overall metrics. You can export the data if you need it for reports or other analysis.
the ui’s confusing at first, but you’ll get used to it pretty quick. double-check your column names before uploading - i got burned on this my first time. also watch your api limits. evaluations burn through credits fast if you’re not paying attention.
Been dealing with similar evaluation workflows for years and honestly, clicking through LangSmith manually gets old fast. You’ll waste tons of time babysitting evaluations.
Automating this whole process is a game changer. I built a workflow that handles dataset uploads, triggers evaluations, monitors progress, and sends me Slack notifications when results are ready.
Here’s what I automated: dataset validation before upload, automatic retry logic when evaluations fail, results parsing and comparison across different model versions, and report generation. No more waiting around for evaluations or forgetting to check results.
The workflow connects LangSmith’s API with our internal systems. New model versions get deployed, evaluations kick off automatically. Results get compared against benchmarks and stakeholders get notified if performance drops.
Saved me probably 10 hours per week that I used to spend on manual evaluation management. Plus zero chance of human error messing up configurations.
You can build something similar without code. Connect LangSmith to your notification systems and data storage. Set up triggers based on model updates or scheduled intervals.
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