Metadata Filters and Permission Aware Retrieval
Filter retrieval by date, product or tenant and make sure users only ever retrieve what they may see.
A taste of a lesson
Could we retrieve everything and just instruct the model not to mention restricted documents?
No, that is not access control. Once restricted text is in the prompt, the model may quote it, summarise it or be talked into revealing it, and your logs and caches now hold it too. Apply the permission filter inside the retrieval query, using the user's identity and groups resolved on your server, so restricted chunks are never returned. Then write tests where a user without access searches for a restricted document's exact title. What does your source system use to define access: users, groups or roles?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Design chunk metadata for filtering, citations and access control
- Apply filters during search without losing recall
- Extract filters from natural language safely and validate them
- Enforce permissions in retrieval with the real user identity
- Prove isolation with permission tests and audit logs
Lesson plan
- 1 Metadata design Choose the fields each chunk needs for filtering, citation and access. Start
- 2 Filtering mechanics Apply filters correctly and understand their effect on recall. Start
- 3 Filters from natural language Let a model propose filters without creating new risks. Start
- 4 Permission aware retrieval Enforce access rights inside the retrieval query itself. Start
- 5 Tenants, syncing and derived data Keep isolation intact as data and permissions change. Start
- 6 Proving it with tests Build tests and logs that demonstrate correct isolation. Start
Try asking
About this tutor
For experienced developers building retrieval over company documents where results must respect filters and access rights: different customers, departments, regions or confidentiality levels. You learn to design chunk metadata, apply filters during search without losing recall, extract filters from natural language queries safely, and enforce document permissions at retrieval time using the real user's identity. The tutor covers multi tenant isolation choices, syncing permission changes from source systems, derived data such as summaries and embeddings, and a test suite that proves user A can never retrieve user B's documents.
Reviews
4.7
3 ratingsSample
- Karin V.Sample
Our shared index relied on developers remembering the tenant filter. We moved it into one central function and added the permission test suite from lesson six.
- Obinna E.Sample
The point that summaries and caches inherit permissions was something nobody on our team had considered. Thorough and serious.
- Martina G.Sample
Clear on pre versus post filtering. Revocation syncing was the most useful part for us. Dense but worth it.
About the teacher
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9 tutors 374 lessons taught Sample
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split,...
See Emeka's profile and tutorsMore like this
Other tutors on the same or nearby topics.