Lumina Editorial
Field notes for decisions that need evidence.
For people building AI systems or researching with data: practical guides for making questions, analyses, failures, and decisions more verifiable.
Choose your starting point
Let the problem choose where you start.
Each path stands on its own. Read one article now, then use related links when you need more depth.
Quality and release
Start here to turn evaluations into auditable decisions.
Architecture and interface
Choose the right complexity and make evidence usable.
Development and operations
Take agents from repository to traffic with explicit controls.
Applied health statistics
Start from the question and build an analysis another researcher can review.
All articles · 08
LLM quality gates: turn evaluations into release decisions
A practical framework for combining deterministic checks, LLM judges, trace evidence, and explicit PROMOTE, HOLD, or ROLLBACK decisions.
LLM application release checklist: evidence before traffic
A practical pre-release checklist for LLM applications covering contracts, evals, security, observability, ownership, and rollback.
How to evaluate an LLM judge before trusting it
Validate an LLM judge with anchored rubrics, labeled cases, bias tests, disagreement, abstention, and change control.
From chatbot to decision system: when to use multi-agent architecture
A practical decision framework for choosing a workflow, one agent, or multiple specialized agents in conversational systems.
Beyond text: generative UI for conversational systems
How to turn model and tool outputs into trustworthy charts, tables, metrics, and actions without letting an LLM invent the interface contract.
How to put coding agents to work safely
A practical adoption playbook for coding agents using repository contracts, reusable skills, independent review, permissions, hooks, and evidence gates.
Shipping an agentic application on Google Cloud (GCP) with evidence gates
A production pattern for agentic applications using exact build boundaries, Cloud Build, Artifact Registry, Cloud Run readiness, and rollback.
Choose a statistical method without starting from the test
Why health analyses need an evidence trail before method selection: question, study design, dependence, effect, uncertainty, and limits.