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Description

DeepEval offers an intuitive open-source framework designed for the assessment and testing of large language model systems, similar to what Pytest does but tailored specifically for evaluating LLM outputs. It leverages cutting-edge research to measure various performance metrics, including G-Eval, hallucinations, answer relevancy, and RAGAS, utilizing LLMs and a range of other NLP models that operate directly on your local machine. This tool is versatile enough to support applications developed through methods like RAG, fine-tuning, LangChain, or LlamaIndex. By using DeepEval, you can systematically explore the best hyperparameters to enhance your RAG workflow, mitigate prompt drift, or confidently shift from OpenAI services to self-hosting your Llama2 model. Additionally, the framework features capabilities for synthetic dataset creation using advanced evolutionary techniques and integrates smoothly with well-known frameworks, making it an essential asset for efficient benchmarking and optimization of LLM systems. Its comprehensive nature ensures that developers can maximize the potential of their LLM applications across various contexts.

Description

Promptfoo proactively identifies and mitigates significant risks associated with large language models before they reach production. The founders boast a wealth of experience in deploying and scaling AI solutions for over 100 million users, utilizing automated red-teaming and rigorous testing to address security, legal, and compliance challenges effectively. By adopting an open-source, developer-centric methodology, Promptfoo has become the leading tool in its field, attracting a community of more than 20,000 users. It offers custom probes tailored to your specific application, focusing on identifying critical failures instead of merely targeting generic vulnerabilities like jailbreaks and prompt injections. With a user-friendly command-line interface, live reloading, and efficient caching, users can operate swiftly without the need for SDKs, cloud services, or login requirements. This tool is employed by teams reaching millions of users and is backed by a vibrant open-source community. Users can create dependable prompts, models, and retrieval-augmented generation (RAG) systems with benchmarks that align with their unique use cases. Additionally, it enhances the security of applications through automated red teaming and pentesting, while also expediting evaluations via its caching, concurrency, and live reloading features. Consequently, Promptfoo stands out as a comprehensive solution for developers aiming for both efficiency and security in their AI applications.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

OpenAI Yes 
Cake AI No 
Claude No 
Hugging Face Yes 
KitchenAI Yes 
LangChain Yes 
Llama 2 Yes 
LlamaIndex Yes 
Netguru Omega No 
Opik Yes 
Ragas Yes 
Weaviate Yes 

Integrations

OpenAI Yes 
Cake AI Yes 
Claude Yes 
Hugging Face No 
KitchenAI No 
LangChain No 
Llama 2 No 
LlamaIndex No 
Netguru Omega Yes 
Opik No 
Ragas No 
Weaviate No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

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 Yes 
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) Yes 
In Person Yes 

Vendor Details

Company Name

Confident AI

Country

United States

Website

docs.confident-ai.com

Vendor Details

Company Name

promptfoo

Country

United States

Website

www.promptfoo.dev/

Product Features

Product Features

Alternatives

Alternatives

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