Freeplay

What is Freeplay?

Freeplay is a comprehensive platform designed to empower AI teams to accelerate the development and deployment of AI-powered products. It unifies critical workflows such as prompt and model versioning, custom evaluation creation, real-time observability of LLM interactions, and automated testing within a single system. By facilitating collaboration between engineers and domain experts, Freeplay streamlines experimentation, continuous improvement, and production monitoring, ensuring high-quality AI product delivery without the friction of switching between multiple tools.

Key Features

• Prompt & Model Management

  Version, deploy, and experiment with prompt and model changes like feature flags, enabling rigorous and controlled AI development.

• Custom Evaluations

  Create and fine-tune evaluation metrics tailored to your product’s quality standards to accurately measure AI performance.

• LLM Observability

  Instantly search, review, and analyze any LLM interaction from development through production to gain full visibility into AI behavior.

• Automated Testing & Experiments

  Run batch tests and auto-evaluations to quantify the impact of prompt and model changes, supporting a culture of continuous experimentation.

• Customizable Playground

  Craft and compare prompts across multiple LLM providers in a flexible environment to optimize AI outputs.

• Data Labeling & Dataset Management

  Label results and curate data sets seamlessly within the platform to support testing, fine-tuning, and quality assurance workflows.

Use Cases

• AI Product Development : Enable cross-functional teams to collaboratively build and iterate on AI-powered features with version-controlled prompts and models.
• Model Performance Evaluation : Design custom evaluations and automate testing to ensure AI models meet specific quality and reliability criteria.
• Production Monitoring : Monitor live AI interactions with full observability to quickly detect issues and maintain product quality in real time.
• Prompt Optimization : Experiment with prompt variations and compare outputs across different LLM providers to optimize AI responses.
• Data Labeling and Quality Assurance : Streamline data labeling workflows and manage datasets to support continuous improvement and fine-tuning of AI models.

FAQs