Guardrails is an open-source Python package designed to validate and correct outputs of large language models (LLMs). It introduces “rail spec,” allowing users to define expected structure and types, including quality criteria for bias and bugs. Its notable features include compatibility with various LLMs, Pydantic-style validation, and real-time streaming support. Guardrails provides a valuable solution for ensuring reliable AI outputs.
Guardrails: An Open-Source Python Package for Validating and Correcting Large Language Models (LLMs) Outputs
In the world of artificial intelligence, ensuring the reliability and quality of outputs from large language models (LLMs) is a common challenge for developers. Guardrails, an open-source Python package, addresses this challenge by providing a practical solution to validate and correct the outputs of LLMs.
Key Features of Guardrails:
- Introduces the concept of a “rail spec” to define the expected structure and types of LLM outputs.
- Compatible with various LLMs, including popular ones like OpenAI’s GPT and Anthropic’s Claude, as well as any language model available on Hugging Face.
- Offers Pydantic-style validation to ensure outputs conform to the specified structure and predefined variable types.
- Supports streaming for real-time validations without waiting for the entire process to complete.
Practical Application of Guardrails:
Guardrails allows developers to set up corrective actions when the output fails to meet the specified criteria. For example, it can prompt the LLM to generate a new valid name if a generated pet name exceeds the defined length.
Value and Practical Use:
By integrating Guardrails into their workflows, developers can enhance the accuracy, relevance, and quality of AI-generated content with greater confidence and efficiency.
Meet Guardrails for AI Evolution and Automation
If your company aims to stay competitive and leverage AI for your advantage, consider adopting Guardrails as a solution to enhance AI-generated content’s accuracy and quality.
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