Library
Field notes on AI-assisted publishing: governance, retrieval architecture, content operations, cost control and the engineering habits that make content platforms trustworthy.
Governance is not a document you write after launch. It is a set of controls wired into the workflow itself: who may approve, what gets logged, and what a model is never allowed to do alone.
Bolting a vector database onto an app does not give you grounded answers. Retrieval-augmented generation works when the corpus, the chunking strategy and the contract between retriever and generator are designed together.
Most migration pain comes from moving content before modelling intent. Get the content model right and the move becomes mechanical. Get it wrong and you pay for a decade in validation scripts.
Regulated content teams are told to slow down. The high performers do the opposite: they industrialise review trails, template the compliance language and automate the checks humans are bad at.
Adaptive experiences do not require surveillance. Structured content metadata plus a small set of declared audience profiles delivers most of the value with none of the creepiness.
LLM features fail economically before they fail technically. Token accounting, caching discipline and right-sized models keep AI features inside budgets that survive their first invoice.
Static-first rendering at the edge serves content-platform traffic from points close to users, survives origin outages and costs a fraction of always-on server fleets. The trade-offs are manageable and worth naming.
Accessible products are not audited into existence once a year. They emerge from semantic HTML, tested keyboard paths and contrast decisions made at design-token level.
Part of the Vuva Systems portfolio — vuvasystems.com