Galileo AI
by Galileo
Galileo is an AI evaluation and observability platform for generative AI and LLM applications, providing automated metrics to detect hallucinations, measure output quality, and monitor RAG and agent pipelines in development and production.…
Definition
Galileo is an AI evaluation and observability platform for generative AI and LLM applications, providing automated metrics to detect hallucinations, measure output quality, and monitor RAG and agent pipelines in development and production. It focuses on giving teams quantitative, real-time signals about generative AI reliability without requiring manual review of every output.
Overview
As teams move generative AI applications from prototypes to production, a central challenge is quantifying output quality at scale, since LLM responses are open-ended and traditional accuracy metrics don't directly apply. Galileo was built to address this by providing a suite of proprietary and research-backed evaluation metrics — covering issues such as hallucination, context adherence, chunk relevance in RAG pipelines, and prompt injection risk — that can be computed automatically on every request rather than relying solely on manual spot-checks. Galileo's platform spans the development and production lifecycle: during development, teams can run evaluations against test datasets to compare prompts, models, or RAG configurations before shipping; in production, the same metrics run continuously on live traffic, surfacing quality regressions, anomalous outputs, or emerging failure patterns in near real time. This is particularly valuable for RAG and agentic applications, where failures can stem from multiple stages (retrieval, generation, tool use) and pinpointing which stage caused a bad output is often the hardest part of debugging. A distinguishing aspect of Galileo's approach is its focus on 'guardrail metrics' engineered to run efficiently and cheaply enough to evaluate every production request, rather than sampling, aiming to give teams confidence that quality issues will be caught as they happen rather than discovered later through user complaints. Galileo competes in the LLM evaluation and observability space with platforms like Arize AI, Braintrust, and WhyLabs, generally differentiating through its emphasis on purpose-built, fine-grained metrics for hallucination and RAG-specific failure modes computed at production scale.
Key Features
- Automated metrics for hallucination detection and output quality scoring
- RAG-specific evaluation covering retrieval relevance and context adherence
- Real-time production monitoring designed to run on every request, not just samples
- Development-time evaluation for comparing prompts, models, and pipeline configurations
- Guardrail metrics engineered for low-latency, high-throughput evaluation
- Support for evaluating multi-step agent and tool-use workflows
- Dashboards for tracking quality trends and regressions over time
- Prompt injection and safety risk detection