The blog post discusses the evaluation of large-language-model (LLM) applications using two tools—Deepchecks LLM Evaluation and Amazon Bedrock Evaluations—focusing on retrieval-augmented generation (RAG) pipelines. Deepchecks offers continuous monitoring and real-time quality assurance, integrating with AWS SageMaker for both development and production stages, while Amazon Bedrock provides on-demand, batch evaluation jobs with a focus on quality, safety, and citation metrics. Deepchecks emphasizes automatic scoring, comprehensive metric evaluation, and real-time alerts to track model performance and detect issues like hallucinations and policy violations. In contrast, Amazon Bedrock evaluates RAG applications through batch jobs, providing a quick, pay-as-you-go approach suitable for A/B testing and prompt experiments. Both tools complement each other in providing end-to-end confidence across different stages of the LLM lifecycle, with Deepchecks offering in-depth analysis and continuous evaluation, while Bedrock excels in fast, iterative evaluations.