Practical LLM Evaluation: Deepchecks and Bedrock

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.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.