agentic RAG knowledge retrieval

RAG & Knowledge Retrieval

Retrieve authoritative, permission-aware evidence before asking a model to answer or act.

DIFFICULTY
Intermediate
EST. TIME
65 min
UPDATED
2026-08-19
On this page
  1. 01Definition
  2. 02Why it matters
  3. 03Step by step
  4. 04Build it
  5. 05Failure modes
  6. 06Production notes
  7. 07Sources

Learning objectives

  • Design ingestion, retrieval, reranking, and citation as separate stages
  • Apply access controls before retrieved content reaches the model
  • Evaluate retrieval quality independently from answer quality

Prerequisites

Embeddings and search basics · A document source with known permissions

Answer first

Canonical definition

Retrieval-augmented generation supplies a model with selected evidence from an external knowledge source at run time so the response can be grounded, current, and traceable.

Operating context

Why it matters

The model cannot compensate for missing, stale, or unauthorized evidence. Retrieval quality is an upstream product decision.

Enterprise RAG must preserve document and user permissions throughout indexing, retrieval, caching, and citation.

Method

Step by step

  1. 01

    Prepare the corpus

    Track source, owner, version, effective date, access policy, and deletion behavior before chunking content.

  2. 02

    Retrieve with filters

    Apply tenant, user, document, time, and policy filters before semantic or lexical ranking.

  3. 03

    Assemble evidence

    Rerank for the question, deduplicate, preserve citations, and fit evidence within a bounded context budget.

  4. 04

    Evaluate both layers

    Measure whether the right evidence was retrieved and whether the final answer used it faithfully.

Hands-on lab

Build a permission-aware answer flow

Create two users with different document access and verify that retrieval and citations never cross the boundary.

Deliverable

An ingestion schema, retrieval pipeline, access tests, citation UI, and a small eval set.

What breaks

Common failure modes

  • F1Filtering documents after retrieval or generation
  • F2Embedding stale content without version and deletion propagation
  • F3Evaluating fluent answers without checking retrieved evidence

Beyond the demo

Production notes

  • Permission metadata must survive chunking, vector storage, reranking, caches, and generated citations.
  • Monitor retrieval misses, empty results, source freshness, and unauthorized-hit tests as first-class signals.

Further reading

Sources

  1. [1]Tenten: Document-level permissions in RAG
  2. [2]NIST AI RMF

Related Tenten resources

From capstone to production

A working demo is the beginning of the engineering problem.

Tenten helps teams scope one agentic workflow, integrate real data and permissions, define evals and handoffs, and own the path through launch and on-call.