Snowglobe: AI Simulation Environment for LLM Testing

[baoshi.rao]

Introduction

Snowglobe is an AI simulation environment for testing Large Language Model (LLM) applications at scale. It enables teams to simulate real-world user behavior, catch edge cases early, and improve model performance before deployment. Visit Snowglobe.

What is Snowglobe?

Snowglobe is designed for LLM teams to test how their AI applications respond to real-world user behavior. It allows users to run full workflows through realistic scenarios, identify risks, and improve model performance.

How to Use Snowglobe

1. Connect your conversational AI agent via API or SDK.
2. Configure simulations with realistic personas and scenarios.
3. Run hundreds of conversations and analyze results.
4. Generate judge-labeled datasets for evaluation and fine-tuning.

Core Features

• Realistic user persona and scenario generation
• Large-scale conversation simulation (hundreds in minutes)
• Automated evaluation with built-in and custom metrics
• AI risk identification (e.g., hallucination, toxicity)
• Agent execution for end-to-end conversations

Use Cases

1. Generating Eval Sets for Chatbots: Create judge-labeled test datasets from simulated conversations.
2. Generating Fine-tuning Datasets: Produce high-signal training data.
3. QA at Release Speed: Catch issues by running hundreds of realistic conversations per build.
4. Testing for AI Risks: Identify and address risks like hallucination and toxicity.
5. High-Stakes Contexts: Verify risks for legal professionals.

Pricing

• Self-service: $0.25 per generated message (after first 250 free).
• Enterprise: Contact for custom pricing, including advanced features and support.

FAQ

• What is chatbot conversation simulation?
• How does Snowglobe help with chatbot evaluation?
• Can Snowglobe generate training data for fine-tuning?

For more details, visit Snowglobe.