Pi Labs

What is Pi Labs?

Pi Labs offers an AI-powered platform designed to automatically build evaluation systems (evals) for AI applications, particularly those involving Large Language Models (LLMs) and agents. It enables users to create custom scoring models that precisely match user feedback and prompts, ensuring highly accurate and consistent evaluation. The platform integrates seamlessly with various existing tools and provides a fast, highly accurate foundation model called Pi Scorer for comprehensive metrics, observability, and agent control across the entire AI stack.

How to use Pi Labs?

To use Pi Labs, you first work with Pi’s copilot to build your custom scoring system. This involves feeding it your prompts, PRDs, or user feedback, or simply chatting with it to define the best calibrated metrics for your application. Once the scoring system is established, you can then use it to evaluate anything across your AI stack, including offline evaluations, online inference, training data quality, model optimization, and agent control flows.

Pi Labs’s Core Features

Automatically builds evaluation systems (evals) to match user feedback and prompts. Provides accurate and consistent scoring, unlike variable LLM-as-judge methods. Integrates with various tools like Sheets, PromptFoo, GRPO, and CrewAI. Intelligently identifies what metrics to measure for your application. Features Pi Scorer, a foundation model that scores more accurately than Deepseek and GPT 4.Offers extremely fast scoring, processing 20+ custom dimensions in less than 100ms. A single scorer can be used across the entire AI stack (offline evals, online observability, training data quality, model optimization, agent control flows). 32K context window for Pi Scorer. Currently supports text-only evaluation (other modalities coming soon).

Pi Labs’s Use Cases

• Evaluating user feedback and prompts for AI applications.
• Scoring examples like news articles and their summaries.
• Assessing the performance of AI agents (e.g., Trip Planning Agent, Product Marketing Agent Comparison).
• Evaluating blog posts based on specific stylistic requirements.
• Conducting offline evaluations and online inference for AI models.
• Assessing training data quality.
• Optimizing AI models.
• Managing agent control flows.