Search Results for "traceability"

Efficiently integrate secure, performant, and cost-effective Generative AI workflows into business value. Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines. Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows. A four-step assessment for grading generative AI systems around performance, features, trustworthiness and enterprise-grade readiness.

LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and validation via automatically generated interactive HTML visualizations. LangExtract supports a wide range of models, including Google Gemini, OpenAI GPT, and local LLMs via Ollama, making it adaptable to different deployment environments and compliance needs. ...

...It codifies best practices—building images, running unit/integration tests, publishing artifacts, and rolling out changes—so teams can adopt a consistent CI/CD approach with minimal Groovy code. The library includes helpers for preview environments, GitOps-style promotion, and release versioning to improve traceability across stages. Because it abstracts low-level CLI invocations, pipelines become more declarative and easier to review. It encourages composable, parameterized pipelines that work across many repositories while still allowing per-service overrides. Organizations use it to bootstrap platform-aligned pipelines quickly and reduce drift in how services are built and deployed.