Evals

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: Voice agent evaluation requires assessing speech recognition accuracy, response latency, conversation flow quality, interruption handling, and goal completion across multi-turn dialogues. Effective evaluation demands visibility into ASR/TTS quality, tool calls, LLM reasoning, and real-time performance metrics. This guide compares the top five voice evaluation platforms: Maxim AI,

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.

TL;DR Choosing the right AI evaluation platform significantly impacts development velocity and agent quality. This analysis compares five leading platforms: Maxim AI, Langfuse, Arize, Galileo, and Braintrust. While most platforms require SDK integration into your codebase, Maxim uniquely offers HTTP API endpoint-based testing, allowing teams to evaluate agents through