Corpus Design Principles for AI Systems
Many AI discussions focus on model selection, prompting techniques, vector databases, and agent frameworks.
- Type
- Architecture
- Last updated
- 2026-06-06
Technical Library
Explore the ideas behind the work: reliability tradeoffs, AI architecture, operational trust, workflow design, and synthetic incident analysis.
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Many AI discussions focus on model selection, prompting techniques, vector databases, and agent frameworks.
John Walpole builds practical AI, automation, and reliability systems with an emphasis on grounded retrieval, explainability, source quality, and operational maturity.
A production Kubernetes environment experienced a service outage following a node provisioning configuration change.
Much of the discussion around artificial intelligence focuses on model size, benchmark scores, reasoning capabilities, and rapid feature development.
Engineering and operations teams often lose time searching across scattered knowledge sources when troubleshooting systems, reviewing changes, responding to incidents, or answering operational questions.
One of the most common questions in modern AI architecture is:
One of the most common failure modes of modern AI systems is presenting confident answers without sufficient grounding in trusted information.
As artificial intelligence becomes increasingly integrated into software development, operations, infrastructure, and business processes, ethical considerations become difficult to ignore.
Many AI applications are built around a simple interaction model: