// library

How the work is actually done

In short

The library is where we write down how the work is actually done — the decisions behind an AI build, what breaks in production, and how to tell a good supplier from a confident one. Everything here is grouped by topic, and every page answers one question.

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Retrieval and grounding: answering from your own contentEverything that happens at query time — how a passage is chosen, ranked, filtered by permission and cited — and how you tell 'the wrong documents came back' apart from 'the right documents, badly used'; the plumbing that gets content into the index lives in the data cluster, and eval methodology lives in the release-gate cluster.20 pagesData readiness, pipelines and keeping the corpus trueThe unglamorous half of every AI build — whether the data is usable at all, how it gets in, how it stays fresh, and how deletions, duplicates and schema drift are handled before anything is indexed or inferred over; one-off migrations of a legacy dataset belong to the integration cluster.16 pagesChoosing the shape of the systemThe decision made before any code exists: what problem shape this actually is, whether it needs an agent loop, a fixed pipeline, a classifier or no model at all, how much autonomy the task can carry, and whether prompting, retrieval or training is the right lever — which process to automate first is an organisational question and sits in the adoption cluster.2 pagesDeciding what to build, and who builds itThe pre-engagement decision — whether to build, buy or configure, whether to hire in-house or bring in a partner, how studios, consultancies, agencies and freelancers actually differ, and how to justify the spend and run diligence on a vendor whose security posture rests on controls rather than certificates.6 pages

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