Here is the honest situation. Here is the honest situation. Retrieval-augmented generation moves the hard problem off the model and onto the data, and most teams are not set up for that. The corpus decides the answer, so a stale document, a mis-chunked passage, a near-duplicate that crowds the results, or a page nobody checked the rights on becomes a confident wrong answer at scale. Retrieval that ignores permissions turns the assistant into a confused deputy that hands any user any indexed document. An index that only knows how to add keeps serving content that was retracted at source weeks ago. An openly writable ingestion path lets someone else decide what your system says. And because the output is fluent, none of this announces itself. Where teams fall short is predictable: a corpus assembled by whatever the ingestion job could reach, permissions copied once and never refreshed, no deletion path at all, chunking left at a library default, and quality judged by trying a few questions by hand instead of measuring retrieval and groundedness against a real evaluation set.
This Kit removes the guesswork. It is RAG data governance and pipeline quality written as adopt-ready controls you personalize in a weekend, with the evidence a reviewer examines.
What you get, the moment you buy
Grounded in how production retrieval systems actually fail. Editable Word and Excel files.
What one control looks like
This is the opening control, where the programme begins. All 18 are built to this depth.
Why this is not another template pack
- The evidence is the point. A control you cannot evidence is a gap waiting to be found. This tells you what a reviewer examines and where teams fall short, for every control.
- The RAG specifics built in. Provenance and rights, chunking and cleaning, deduplication, metadata validation at the index boundary, per-user retrieval filtering, update and deletion propagation, indirect injection, and retrieval and groundedness measurement are written into the controls, not left generic.
- Built on real practice, not one person's opinion, grounded in how production retrieval systems actually hold up and where they actually fail.
- It compounds. This work shares its shape with data protection, AI governance and information security frameworks, so it feeds your wider programme.
Who buys this
Data and machine learning engineers who own a production retrieval system, and the platform, security and data governance leads accountable for what it indexes and what it returns. Whether you are hardening a system already in front of users or designing the data layer before launch, you save weeks and walk in with your source, ingestion, access, freshness, integrity and evaluation controls structured.
Common questions
Is it really editable? Yes. Word and Excel files you own and adapt. No portal, no subscription.
Does it cover retrieval access control? Yes. Keeping sensitive content out of the index, carrying permission metadata on every chunk, filtering results against the requesting user's entitlements, and re-synchronising when access changes at source each have their own control with its own evidence.
Does it cover data poisoning and prompt injection through retrieved content? Yes. Controlling every pathway that writes into the corpus, treating retrieved passages as data rather than instruction, and monitoring the corpus for anomalies are all built as controls.
What if it is not for me? A 30-day money-back guarantee.
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