Arrow AI system architecture: from data to dependable workflows
A practical reference architecture for AI systems: source data, retrieval, generation, permissions, workflow execution, and evaluation.
Read guide →The technology behind useful AI.
Practical guides to sources, answer quality, and dependable workflows. Original engineering perspectives from Arrow AI.
Guides and articles
Reliable AI answers: sources, permissions, and evaluation
How to design an AI answer system with traceable evidence, permission-aware retrieval, useful uncertainty, and a repeatable evaluation process.
Read guide →Before building RAG, define a source contract
A concrete source contract for RAG: document ownership, effective dates, permission filters, versioning, deletion, and an example retrieval trace.
Read article →How to evaluate AI answers beyond a single score
A worked AI answer evaluation matrix covering retrieval relevance, factual support, citation quality, abstention, and permission boundaries.
Read article →AI workflow retries: prevent duplicate actions with idempotency
Design AI workflows that handle ambiguous failures: durable operation records, idempotency keys, bounded retries, approval, and reconciliation.
Read article →How we write
Published by Arrow AI. We cite primary documentation, label synthetic examples, and distinguish proposed architectures from product claims. Each article shows its publication and review date.