What is Scorecard.io?
Scorecard.io offers a comprehensive platform for navigating the entire AI production lifecycle, focusing on testing and evaluating Generative AI systems like LLMs, RAG systems, agents, and chatbots. It assists developers in ensuring their applications are ready for production by providing tools for experiment design, system prototyping, testset development, metric development, and continuous evaluation. The platform emphasizes shipping products with confidence through features like A/B analysis and prompt iteration management.
It facilitates metric creation and validation, allowing users to evaluate systems using a library of vetted metrics or design custom AI-powered metrics simply by describing them. Scorecard.io integrates human labeling for ground truth validation when high accuracy is critical. The platform also streamlines prompt engineering by enabling users to build, manage, compare, and productionize prompts effectively within a dedicated playground and management system. Integration is simplified with native SDKs for Python and Typescript, allowing developers to incorporate Scorecard into production deployments quickly.
Features
- A/B Comparison: Effortlessly compare experiments and system versions.
- Metric Development: Create, validate, and productize evaluation metrics using pre-vetted libraries or custom AI-powered instructions.
- Human Labeling: Integrate human graders for ground truth validation of mission-critical applications.
- Prompt Engineering & Management: Build, manage, compare, version control, and productionize prompts.
- Scorecard Playground: Experiment with models and prompts from various providers.
- Testset Management: Develop and manage test datasets for evaluation.
- Logging and Tracing: Monitor and debug AI systems.
- SDK Integration: Easily integrate with production deployments using Python and Typescript SDKs.
- Collaboration Tools: Facilitate team collaboration and project management.
- Enterprise Readiness and Compliance: Features designed for enterprise needs.
Use Cases
- Evaluating the performance and readiness of LLM applications before deployment.
- Testing and improving Retrieval-Augmented Generation (RAG) systems.
- Developing and assessing the effectiveness of AI agents.
- Validating the quality, correctness, and helpfulness of chatbots.
- Comparing different versions of prompts or models using A/B testing.
- Creating and managing robust evaluation metrics for AI systems.
- Ensuring AI application accuracy through human feedback integration.
- Streamlining the prompt engineering lifecycle for AI development teams.
- Monitoring and debugging AI systems during development and production.
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Scorecard.io Uptime Monitor
Average Uptime
100%
Average Response Time
228.73 ms
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