OpenAI Evals

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OpenAI Evals is an open-source framework for evaluating large language models (LLMs) and LLM systems. It provides a registry of benchmarks and allows users to create custom evaluations for specific use cases.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is OpenAI Evals?

OpenAI Evals offers a comprehensive framework for evaluating large language models (LLMs) and systems built using them. It includes an open-source registry of benchmarks to test various dimensions of OpenAI models. Users can also develop their own custom evaluations tailored to specific use cases, and even build private evaluations using their own data without public exposure. The framework emphasizes the importance of high-quality evaluations for understanding how different model versions impact a use case, making it a critical tool for anyone building with LLMs. It supports running and creating evals, with options for logging results to a Snowflake database and integrating with Weights & Biases.

Best used for

Ideal for product managers and startup founders who need to rigorously evaluate the performance of large language models, create custom benchmarks for their specific applications, and understand the impact of different model versions. Especially valuable for ensuring the quality and reliability of LLM-powered systems.

Common actions

evaluate LLM performance

create custom benchmarks

test AI models

deepfakeautomated workflowlow-code/no-codeopen-sourcecollaborationface swapping"AI Agents"github copilotworkflows

Capabilities

Key features

Evaluate LLMs
Open-source benchmark registry
Custom evaluation creation
Private data evaluation
Log results to Snowflake
Weights & Biases integration

Target Audience

product managerstartup founder

Integrations

weights-biasessnowflake

Pricing & Plans

Open Source

Free

FAQs

What is the primary purpose of OpenAI Evals?

OpenAI Evals provides a framework for evaluating large language models (LLMs) and LLM systems. It includes an open-source registry of benchmarks and allows users to create custom evaluations to test different dimensions of AI models and their applications.

Can I use my own data to create evaluations with OpenAI Evals?

Yes, you can use your own data to build private evaluations. This allows you to represent common LLM patterns in your workflow without exposing any of that data publicly, ensuring privacy and relevance to your specific use cases.

What are the minimum requirements to run OpenAI Evals?

To run OpenAI Evals, you will need Python 3.9 or higher. You also need to set up and specify your OpenAI API key using the OPENAI_API_KEY environment variable, and be aware of associated API costs.

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