Relari: Testing and Simulation Stack for GenAI Systems
Relari is an open-source platform that provides a comprehensive testing and simulation stack to evaluate, validate, and improve complex Generative AI (GenAI) applications throughout the development lifecycle.
https://www.relari.ai/
Product Information
Updated:Nov 9, 2024
What is Relari: Testing and Simulation Stack for GenAI Systems
Relari is a data-driven toolkit designed to help AI teams rigorously test and optimize GenAI applications like RAG systems, LLM agents, chatbots, and more. Founded by experts in AI system production from MIT and Harvard, Relari offers an open-source evaluation framework along with a cloud platform for generating custom synthetic data and simulating user behavior. The platform aims to address the challenges of ensuring reliability and performance in complex AI systems, especially for mission-critical applications in industries like healthcare and finance.
Key Features of Relari: Testing and Simulation Stack for GenAI Systems
Relari is a comprehensive testing and simulation stack for Generative AI (GenAI) applications, offering tools for simulating, testing, and validating complex AI systems throughout the development lifecycle. It provides an open-source evaluation framework, synthetic data generation capabilities, custom metrics, and a cloud platform for stress testing and hardening GenAI applications, enabling AI teams to improve reliability and performance efficiently.
Open-source evaluation framework: Continuous-eval, a modular framework with metrics covering various LLM use cases including text generation, code generation, retrieval, classification, and agents.
Synthetic data generation: Custom synthetic dataset creation tool to simulate diverse user behaviors and generate massive test sets for thorough validation.
Cloud-based simulation platform: A platform that allows teams to stress test and harden GenAI applications by simulating user behavior in custom evaluation pipelines.
Component-level evaluation: Capability to evaluate and provide metrics for each step of a GenAI pipeline, going beyond simple observability.
Auto prompt optimizer: Tool to automatically optimize prompts for improved performance in GenAI applications.
Use Cases of Relari: Testing and Simulation Stack for GenAI Systems
Enterprise search engine testing: Using synthetic datasets to stress test and guide product decisions for enterprise search engines powered by GenAI.
Financial services AI validation: Rigorously testing and validating AI systems used in financial services to ensure reliability and accuracy.
Autonomous vehicle simulation: Applying GenAI testing methodologies inspired by autonomous vehicle industry practices to ensure safety and performance.
Chatbot development and optimization: Simulating millions of conversations to test chatbot capabilities and identify flaws in various scenarios.
Healthcare AI system validation: Ensuring the security and dependability of AI-powered medical diagnostic tools through comprehensive testing.
Pros
Comprehensive suite of tools for GenAI testing and validation
Data-driven approach to improve AI system reliability
Flexible framework adaptable to various GenAI applications
Cost-effective alternative to expensive LLM-as-a-judge evaluations
Cons
Potential learning curve for teams new to advanced AI testing methodologies
May require integration efforts for existing AI development pipelines
How to Use Relari: Testing and Simulation Stack for GenAI Systems
Install continuous-eval: Install Relari's open-source evaluation framework 'continuous-eval' by running: git clone https://github.com/relari-ai/continuous-eval.git && cd continuous-eval poetry install --all-extras
Generate synthetic data: Create a free account on Relari.ai and use their cloud platform to generate custom synthetic datasets that simulate user interactions for your specific use case (e.g. RAG, agents, copilots)
Define evaluation pipeline: Use continuous-eval to set up an evaluation pipeline that tests each component of your GenAI application separately, allowing you to pinpoint issues to specific parts of the system
Select evaluation metrics: Choose from Relari's 30+ open-source metrics or create custom metrics to evaluate text generation, code generation, retrieval, classification, and other LLM tasks relevant to your application
Run evaluation: Execute the evaluation pipeline on your synthetic datasets to stress test your GenAI application and identify areas for improvement
Analyze results: Review the component-level metrics and overall system performance to understand where issues originate and prioritize improvements
Optimize prompts: Use Relari's auto prompt optimizer to systematically improve your LLM prompts based on the evaluation results
Iterate and improve: Make targeted improvements to your GenAI application based on the evaluation insights, then re-run the evaluation to measure progress
Monitor in production: Leverage Relari's runtime monitoring capabilities to continuously evaluate and improve your GenAI application's performance in production environments
Relari: Testing and Simulation Stack for GenAI Systems FAQs
Relari is an open-source platform that helps AI teams simulate, test, and validate complex Generative AI (GenAI) applications throughout the development lifecycle. It provides a testing and simulation stack to harden LLM-based applications.
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