Snowglobe

Snowglobe is a simulation environment for LLM teams designed to test how their AI applications respond to real-world user behavior. It enables users to run full workflows through realistic scenarios, catch edge cases early, and confidently improve model performance before deploying to production. Snowglobe helps AI teams test LLM apps at scale by simulating real-world conversations, uncovering risks, and improving overall model performance.

How to Use

To use Snowglobe, users connect their conversational AI agent via API or SDK. The process involves configuring simulations with realistic personas and scenarios, running hundreds of conversations, exploring the results, and analyzing failure patterns and performance metrics. This allows for generating judge-labeled datasets for evaluation and fine-tuning.

Key Features

• Realistic user persona and scenario generation
• Large-scale conversation simulation (hundreds in minutes)
• Automated evaluation with built-in and custom metrics
• Generation of judge-labeled datasets for evals and fine-tuning
• Identification and reporting of AI risks (e.g., hallucination, toxicity)
• Agent execution for end-to-end conversations

Use Cases

• Generating Eval Sets for Chatbots: Create judge-labeled test datasets from simulated user conversations.
• Generating Fine-tuning Datasets: Produce high-signal training data including judge labels, preference pairs, and critique-and-revise triples.
• QA at Release Speed: Run hundreds of realistic conversations per build to catch issues and track error rates.
• Testing for AI risks: Simulate conversations to identify and address risks like hallucination and toxicity.
• Verifying and understanding risk in high-stakes contexts for legal professionals.