Evals

Evaluating AI agents is essential for reliability. Real-world interactions expose non-determinism, model updates, and hallucinations, which degrade trust without rigorous checks. A structured evaluation approach (pre-release and in production) helps quantify quality, prevent regressions, and align systems to human preference. Maxim AI provides unified capabilities across offline testing, online evaluations,

TL;DR * RAG systems combine retrieval and generation; evaluation must assess both components. * Retrieval quality hinges on recall, precision, relevance, and timeliness of sources. * Generation quality requires grounding, faithfulness, clarity, and low hallucination rates. * Judge reliability improves with mixed methods: LLM-as-a-judge, programmatic checks, and human review. * Use Maxim’s offline

The deployment of AI applications in production environments has accelerated dramatically, with organizations across industries racing to integrate large language models (LLMs), voice agents, and retrieval-augmented generation (RAG) systems into their workflows. However, this rapid adoption has exposed a critical challenge: without rigorous evaluation frameworks, AI applications can fail unpredictably,

TLDR LLM-as-a-Judge is an automated evaluation technique that uses large language models to assess and score the outputs of other models. This scalable approach enables nuanced, and rapid evaluations, outperforming traditional metrics and manual review in both speed and depth with scale by reading, reasoning about, and justifying scores across