LLM EVALUATIONS
Build LLM test cases, define quality metrics, and gain instant performance insights
AI evaluations, automated - from dev to production
Build LLM test cases, define quality metrics, and gain instant performance insights
Dataset Creation
Automatically generate test datasets from your production data, low-evals scores, user feedback or annotations scores.
Automated Testing
Define evaluation criteria, run tests automatically in production, and catch regressions before they reach production.
Annotate LLM output
Add annotations to messages for faster debugging, clearer evaluations, and better tracking of model behavior.
A complete LLM Evaluation Suite
From offline testing to real-time monitoring, get complete visibility into your LLM's performance
Streamline Evaluations Pipeline
Run evaluations instantly as messages flow through your LLM system—no delay, no manual steps.
Real-time LLM Quality Checks
Automatically evaluate LLM responses as they happen to ensure quality, safety, and performance in production.
Answer Correctness Checks
Evaluate the factual accuracy and relevance of LLM responses using customizable criteria and reference answers.
LLM-as-a-Judge evaluations
Use language models to automatically assess response quality, enabling scalable and consistent offline testing.
Custom Python-based evaluators
Get immediate quality scores and alerts for each LLM response using your defined evaluation criteria.
RAG Quality
Set up automated responses based on evaluation results, like fallbacks for low-quality outputs.
Evals in your CI/CD pipeline
Run evaluations locally or in CI pipelines to detect regressions, benchmark performance, and validate improvements
Integration with minimal code
Easily connect LangWatch to your LLM pipeline and start streaming evaluation data with just a few lines of code.
Safety checks
Check for PII, prompt injection attempts and toxic content. Setup them up as guardrails or Evaluations
From vibe checking to scalable testing
Enable both engineers and non-technical stakeholders to quickly set up LLM evaluations using:
A no-code Evaluation wizard
Uploads or API integrations
Real-time feedback capture
Create a library of test cases that evolves with your product and customers.
Online, offline and custom evaluations
LangWatch includes ready-to-use online & offline evaluation templates for RAG, prompt chains, and more. You can also build custom evaluation workflows and link them to real-time monitoring.
Whether you're testing a chatbot, retrieval pipeline, or autonomous agent—LangWatch adapts to your LLM stack. Check our docs for online, offline and custom evals.
AI simulations for real-world testing
Continuously test your LLM apps in production-like scenarios. LangWatch simulates user input, edge cases, and adverse prompts giving you pre- and post-deployment performance insights.
Agent to test an Agent using LangWatch Scenarios
Annotations as LLM Evaluations
Simplify human evaluation with centralized annotation workflows. Let experts review generations, flag edge cases, and feed evaluations back into your AI lifecycle.
LangWatch turns annotated feedback into automatic datasets and evaluations
