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Description

Relying solely on a few examples is insufficient for thorough evaluation. To gain actionable insights for enhancing your models, it’s essential to gather extensive end-user feedback. With the improvement engine designed for GPT, you can effortlessly conduct A/B tests on models and prompts. While prompts serve as a starting point, achieving superior results necessitates fine-tuning on your most valuable data—no coding expertise or data science knowledge is required. Integrate with just a single line of code and seamlessly experiment with various language model providers like Claude and ChatGPT without needing to revisit the setup. By leveraging robust APIs, you can create innovative and sustainable products, provided you have the right tools to tailor the models to your clients’ needs. Copy AI fine-tunes models using their best data, leading to cost efficiencies and a competitive edge. This approach fosters enchanting product experiences that captivate over 2 million active users, highlighting the importance of continuous improvement and adaptation in a rapidly evolving landscape. Additionally, the ability to iterate quickly on user feedback ensures that your offerings remain relevant and engaging.

Description

Ragas is a comprehensive open-source framework aimed at testing and evaluating applications that utilize Large Language Models (LLMs). It provides automated metrics to gauge performance and resilience, along with the capability to generate synthetic test data that meets specific needs, ensuring quality during both development and production phases. Furthermore, Ragas is designed to integrate smoothly with existing technology stacks, offering valuable insights to enhance the effectiveness of LLM applications. The project is driven by a dedicated team that combines advanced research with practical engineering strategies to support innovators in transforming the landscape of LLM applications. Users can create high-quality, diverse evaluation datasets that are tailored to their specific requirements, allowing for an effective assessment of their LLM applications in real-world scenarios. This approach not only fosters quality assurance but also enables the continuous improvement of applications through insightful feedback and automatic performance metrics that clarify the robustness and efficiency of the models. Additionally, Ragas stands as a vital resource for developers seeking to elevate their LLM projects to new heights.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Codestral No 
Codestral Mamba No 
DeepEval No 
Gemini No 
Gemini 1.5 Pro No 
Gemini 2.0 No 
Gemini Enterprise No 
Google AI Plus No 
Llama 2 No 
Llama 3.2 No 
MLflow No 
Mathstral No 
Ministral 3B No 
Mistral AI No 
Mistral Small No 
Mixtral 8x22B No 
Mixtral 8x7B No 
Nekton.ai Yes 
Opik No 
Workers by Delos Yes 

Integrations

Codestral Yes 
Codestral Mamba Yes 
DeepEval Yes 
Gemini Yes 
Gemini 1.5 Pro Yes 
Gemini 2.0 Yes 
Gemini Enterprise Yes 
Google AI Plus Yes 
Llama 2 Yes 
Llama 3.2 Yes 
MLflow Yes 
Mathstral Yes 
Ministral 3B Yes 
Mistral AI Yes 
Mistral Small Yes 
Mixtral 8x22B Yes 
Mixtral 8x7B Yes 
Nekton.ai No 
Opik Yes 
Workers by Delos No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Humanloop

Website

humanloop.com

Vendor Details

Company Name

Ragas

Country

United States

Website

www.ragas.io

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