How to Use Datasets and Evaluations in LangSmith - Complete Tutorial

[miar]

I’m trying to understand how to effectively utilize the datasets and evaluation tools in LangSmith. I’ve been reading through the materials, but some aspects remain unclear.

Could someone clarify the process for setting up datasets to test my language model applications? Additionally, I’d appreciate an explanation of how the evaluation system functions and which metrics are crucial to consider.

I’m specifically looking for information on:

• How to create and manage test datasets
• Procedures for running evaluations on my models
• Insight into the various evaluation metrics that are offered
• Recommendations for organizing datasets effectively

Any detailed guidance or examples would be greatly appreciated. I want to ensure I’m approaching this correctly right from the beginning.

[Finn_Mystery]

When I started using LangSmith datasets, the biggest thing I learned was that dataset quality beats quantity every time. Don’t just grab typical examples - focus on edge cases and weird scenarios that’ll break your model. The tracing feature is a game changer. You can see exactly where your model goes off the rails in its reasoning. Skip basic accuracy metrics - they’re pretty useless for conversational stuff. Use semantic similarity and human feedback scores instead. They’ll tell you way more about what’s actually working. Get domain experts involved from day one when you’re building datasets. Trust me, it makes annotation so much easier. And version everything! Your app will change, and you’ll want to see how performance shifts over time.

[Liam_25Meditation]

hey miar! using datasets in langsmith ain’t hard once you get the hang of it. just upload your test cases as csv or json, then run evals on your models. i usually focus on accuracy and latency. start with like 20-50 examples and expand from there.

[Dave_17Sketch]

The Problem:

You’re having trouble effectively using LangSmith datasets and evaluation tools for testing your language model applications. Specifically, you need help creating and managing test datasets, running evaluations, understanding available metrics, and organizing your datasets for optimal results.

Understanding the “Why” (The Root Cause):

The key to effective evaluation in LangSmith lies in understanding that a well-structured, targeted dataset is more valuable than a large, haphazard one. Generic examples won’t reveal the weaknesses in your model. LangSmith’s strengths lie in its ability to facilitate granular analysis and custom evaluation, moving beyond simple accuracy metrics. Pre-built metrics like BLEU or ROUGE often fall short for conversational AI, neglecting crucial aspects like hallucination, relevance, and consistency.

Step-by-Step Guide:

1. Create and Manage Datasets: Instead of uploading files, leverage LangSmith’s web interface for creating datasets. This offers superior control over input-output pairs and metadata tags. Carefully curate your dataset, focusing on edge cases and scenarios likely to challenge your model. Involve domain experts early in the annotation process to ensure data quality. Remember to version your datasets to track performance changes as your application evolves.

2. Run Evaluations: Use LangSmith’s evaluation pipeline to test your models. You can run evaluations directly on your datasets within the LangSmith interface. Consider building custom evaluators to address nuances specific to your application. For instance, create evaluators that specifically assess hallucination, relevance, and consistency. The comparison feature allows for efficient testing of the same dataset against multiple model versions simultaneously. This is invaluable for prompt optimization.

3. Utilize Relevant Metrics: Avoid relying solely on basic accuracy metrics, especially for conversational applications. Instead, prioritize metrics that assess semantic similarity and incorporate human feedback. These provide deeper insights into your model’s performance than simple accuracy scores.

4. Establish a Baseline and Iterate: Begin with a smaller dataset (e.g., 20-50 examples) to establish a performance baseline. Then, iteratively expand your dataset and refine your evaluation criteria based on your findings. LangSmith’s debugging tools allow for token-level analysis, aiding in identifying areas for improvement.

Common Pitfalls & What to Check Next:

• Dataset Bias: Ensure your dataset accurately represents the real-world scenarios your model will encounter. A biased dataset will lead to skewed evaluation results.
• Metric Selection: Carefully choose metrics that align with your application’s specific requirements. Don’t just rely on default metrics; experiment to find what works best.
• Data Versioning: Regularly version your datasets. This is essential for tracking performance changes over time and comparing model versions against consistent data.
• Understanding the limitations of your chosen metrics: No single metric perfectly captures all aspects of model performance. Use a combination of metrics and qualitative analysis to obtain a comprehensive understanding.

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!