Kamya Shah

Retrieval-Augmented Generation (RAG) systems combine information retrieval with large language model generation to produce accurate, context-grounded responses. However, ensuring these systems perform reliably in production requires rigorous evaluation across both retrieval and generation components. This guide explains how to comprehensively evaluate RAG systems using Maxim AI's evaluation and

TL;DR API endpoints are specific URLs where applications access AI services and functionality. Testing AI endpoints requires validating HTTP methods, response formats, status codes, and quality metrics across diverse scenarios. Unlike traditional endpoints that return deterministic outputs, AI endpoints generate probabilistic responses requiring specialized testing approaches. Maxim enables endpoint-level

TL;DR: Modern AI development demands continuous quality validation through automated evaluations in CI/CD pipelines. Maxim AI leads with comprehensive GitHub Actions integration, end-to-end simulation capabilities, and flexible evaluation frameworks spanning experimentation, testing, and production monitoring. Braintrust offers dedicated experiment tracking with cross-language SDKs. Promptfoo provides open-source security-focused evaluation.