Month: 2026-04. Delivered the Aerospace Multimodal Retrieval System and Evaluation for aws-samples/amazon-nova-samples, enabling embedding of images and documents via Amazon Nova Multimodal Embeddings and S3 vectors. Implemented a text-only baseline with OCR-based extraction, and an LLM-as-Judge evaluation framework for retrieval and generation quality. Optimized the retrieval notebook by removing a redundant query prefix, simplifying code, and improving evaluation efficiency. This work accelerates aerospace manufacturing document discovery, strengthens search relevance, and provides a scalable evaluation approach for multimodal retrieval.

Monthly summary for 2025-09 focusing on delivering user-centric policy tooling and improving guidance for LLM workflows in the Bedrock samples repository. Two major features were completed in the aws-samples/amazon-bedrock-samples project, with an emphasis on policy usability and iterative reasoning guidance for developers.

Month 2025-08: Delivered GA release of Bedrock Automated Reasoning Checks for aws-samples/amazon-bedrock-samples, introducing playground notebooks for policy creation, validation, rewriting, and testing; Lambda refactor to integrate guardrails; notebook and client usage refinements; region handling improvements; and enhanced result rewriting and findings handling. This release improves governance, developer experience, and cross-region reliability, enabling faster policy automation and safer Bedrock deployments.

April 2025 performance summary for aws-samples/amazon-bedrock-samples. Delivered three core features to expand Bedrock evaluation capabilities: Bedrock BYOI evaluation enhancements, a Custom Metrics toolkit for Bedrock evaluation, and Automated Reasoning (AR) checks enhancements. Implemented expanded BYOI assets, refined evaluation notebook content, and improved dataset naming and S3 path configurations to support LLMAAJ BYOI evaluations. Introduced a reusable custom metrics framework with code samples and notebooks to guide setup for Bedrock evaluation and RAG workflows. Enhanced AR checks to support multiple foundation models, multiple invocation methods, and improved error handling and regional flexibility. These changes accelerate testing, increase evaluation coverage, and improve decision quality for Bedrock deployments by enabling faster insights, repeatable experiments, and more robust evaluation pipelines.

March 2025 — aws-samples/amazon-bedrock-samples: Key features delivered to enhance Bedrock evaluation pipelines and governance. 1) Evaluation Dataset Schema and RAG Evaluation Workflow Enhancements: renamed referenceContexts to referenceResponses, refined ground-truth text and reference descriptions, and aligned dataset generation to the new schema using a direct Bedrock boto3 client integration (commits: 6ff40fdd1e3882581088f5ebbe3d0bf4b4abca72, def39c05a07c067570456da34a081907d1421978, 3bef7993e0aba647411e7c01c38f5e655d86cc3c, 90c245be3cc1625345d0ef3acc8596f0b1ac4bc0, b23934fa40c0838e6ceb2caa1f0cb41869c48825). 2) Bedrock Automated Reasoning Validation Lambda: introduced an AWS Lambda function to validate AI-generated content against Bedrock's Automated Reasoning policies, with optional corrected responses on violations and accompanying setup instructions (commit: d9ea6ab1a2251b7a2a8c7facf4c1533bcd70f234). 3) BYOI Evaluation Capabilities for Bedrock: added Bring Your Own Inference (BYOI) evaluation capabilities for Bedrock, including notebooks and sample data to evaluate LLM-as-a-Judge scenarios and RAG systems across hosting locations (commit: 3dc3391af09d94d492cb6a683ffb0dbeaa763a69).

February 2025 monthly review for aws-samples/amazon-bedrock-samples: Automated Reasoning (AR) checks with Bedrock Guardrails progressed from initial implementation to ongoing refinements, with setup code, sample policies, environment config, policy loading/validation, and expanded user guidance. No major bugs fixed this month; focus was on delivering a scalable AR-check framework and preparing for production readiness.