Galileo

What is Galileo?

Galileo is an AI evaluation and observability platform focused on helping teams detect hallucinations, measure RAG quality, and monitor production behavior in GenAI applications. It is built for engineers and AI teams that want to move from prompt testing to production guardrails. (docs.galileo.ai)

Understanding Galileo

In practice, Galileo sits in the layer between model development and production operations. Teams use it to trace requests, run evaluations, and inspect whether outputs are grounded in retrieved context, which is especially important for retrieval-augmented generation systems. Its docs highlight observability, evaluation, and production guardrails as core parts of the workflow. (docs.galileo.ai)

Galileo also exposes specialized metrics for RAG systems, including retrieval quality and generation quality measures such as context adherence, context relevance, completeness, and chunk utilization. The platform pairs these metrics with hallucination detection and live monitoring so teams can diagnose issues before they become user-facing failures. (docs.galileo.ai)

Key aspects of Galileo include:

1. Evaluation runs: Test prompts, chains, and RAG workflows before shipping.
2. Hallucination detection: Flag responses that are not grounded in the supplied context.
3. RAG metrics: Measure retrieval relevance, adherence, and completeness.
4. Production observability: Trace live traffic and inspect failures in real time.
5. Guardrails: Turn evaluation results into monitoring and protection workflows.

Common use cases

Teams usually reach for Galileo when they want to make LLM behavior measurable and repeatable.

1. RAG QA: Check whether retrieved passages actually support the final answer.
2. Hallucination tracking: Spot outputs that introduce unsupported claims.
3. Prompt iteration: Compare prompt or chain variants before release.
4. Production monitoring: Watch live traffic for drift, regressions, and risky behavior.
5. Custom evals: Adapt metrics to domain-specific definitions of quality.

Things to consider when choosing Galileo

Galileo is a good fit when your team wants a full evaluation and observability workflow, not just a one-off testing tool.

1. Workflow fit: Check whether you need trace-level observability, offline evals, or both.
2. Metric coverage: Make sure its built-in RAG and hallucination metrics match your use case.
3. Integration surface: Review SDK, API, and framework support for your stack.
4. Production posture: Confirm how you want to use monitoring, alerts, and guardrails.
5. Team adoption: Evaluate whether PMs, researchers, and engineers can all use the same workflow.

Example of Galileo in a stack

Scenario: A support team builds a RAG assistant over internal help docs and wants to reduce unsupported answers.

They log retrieval traces into Galileo, run experiments on prompt variants, and compare context adherence scores across different chunking strategies. When a response looks fluent but unsupported, they inspect the trace, see which retrieved chunks were missed, and tune the retrieval pipeline before redeploying.

After launch, they keep Galileo on as a monitoring layer so production traffic continues to surface hallucinations, low relevance retrieval, and regressions over time.

How PromptLayer helps with Galileo

PromptLayer gives teams a place to manage prompts, track changes, and connect evaluation work back to the people and workflows that ship the application. If Galileo is where you measure quality and guardrail behavior, PromptLayer is where you can organize prompt iteration and keep that process visible across the team.

Ready to try it yourself? Sign up for PromptLayer and start managing your prompts in minutes.