AI Engineer

ScaleCloud resources classified by primary audience role: AI Engineer.

  • Download: RAG Evaluation Starter Pack

    This download bundle helps AI Engineer teams treat retrieval quality, permissions, provenance, and answer evaluation as one design using Test questions and retrieval review in Private Cloud environments. It emphasizes preserve source-level access controls through indexing and retrieval and provides implementation decisions that can be reviewed without relying on vendor or customer claims.

  • Video Tutorial: Add Human Approval to an Agentic Workflow

    This video tutorial helps AI Engineer teams bound tool access, autonomy, memory, and approval points using Tool gateway and approval queue in Microsoft Azure environments. It emphasizes record actions and make consequential steps interruptible and provides implementation decisions that can be reviewed without relying on vendor or customer claims.

  • Video Tutorial: Evaluate RAG Retrieval Before Answer Quality

    This video tutorial helps AI Engineer teams treat retrieval quality, permissions, provenance, and answer evaluation as one design using Retrieval metrics and test sets in Private Cloud environments. It emphasizes preserve source-level access controls through indexing and retrieval and provides implementation decisions that can be reviewed without relying on vendor or customer claims.

  • Deploy a Permission-Aware RAG Retrieval Pipeline

    This technical guide helps AI Engineer teams treat retrieval quality, permissions, provenance, and answer evaluation as one design using Document ACLs, vector search, model gateway in Microsoft Azure environments. It emphasizes preserve source-level access controls through indexing and retrieval and provides implementation decisions that can be reviewed without relying on vendor or customer claims.

  • Recognizing Retrieval Failures in RAG Applications

    This knowledge base article helps AI Engineer teams treat retrieval quality, permissions, provenance, and answer evaluation as one design using Chunking, embeddings, ranking, evaluation in Google Cloud environments. It emphasizes preserve source-level access controls through indexing and retrieval and provides implementation decisions that can be reviewed without relying on vendor or customer claims.