Introduction
Ragas (Retrieval-Augmented Generation Assessment) is an open-source evaluation framework built specifically for measuring the performance of RAG pipelines using fine-grained, model-based and statistical metrics. Ragas provides domain-specific tools to evaluate how effectively a RAG system retrieves, contextualizes, and generates responses—helping teams detect hallucinations, irrelevant citations, weak grounding, or incomplete answers. It is a core part of the evaluation loop for LLM features that rely on context-aware generation over internal knowledge sources.
Key benefits of using Ragas include:
RAG-Specific Evaluation Metrics: Measures critical aspects of retrieval and generation such as faithfulness, answer relevance, context precision, context recall, and retrieval correctness.
LLM-Powered Scoring: Uses large language models to semantically assess answer grounding and contextual alignment—offering more nuanced evaluations than simple keyword matching.
Supports End-to-End and Modular Evaluation: Allows evaluation of full RAG pipelines or broken-down stages (retrieval vs. generation), supporting targeted debugging and optimization.
Integration with LangChain, LlamaIndex, and DeepEval: Fits directly into existing LLMops tooling used at Cake, enabling seamless evaluation during prototyping, testing, and CI/CD workflows.
Custom Dataset and Metric Support: Enables teams to evaluate over custom question/answer datasets, internal documents, or production logs—tailored to Cake-specific domains and quality standards.
Ragas is used to monitor the performance of document Q&A systems, internal knowledge copilots, and agentic workflows that depend on retrieval over structured and unstructured sources (e.g., DataHub, Confluence, Slack archives). It plays a critical role in evaluating grounding quality, minimizing hallucinations, and guiding improvements in retrieval strategies or prompt construction. You can ensure its RAG systems are reliable, grounded, and continually improving—delivering AI-powered answers that are not just fluent, but factually and contextually correct.