Module 2 of 6Lesson 2 of 2~16 min
Inventory sources and metadata
An assistant that cites a draft or an outdated version quickly loses user trust, and the problem often starts with source selection. You learn to inventory a RAG system's content based on real needs, decide what stays out, and settle early the metadata everything downstream will rely on.
Lesson objective
By the end of this lesson, you will be able to build a RAG source inventory from real user questions, decide what is excluded, and list the metadata to capture at ingestion.
Topics covered
- RAG data sources
- metadata
- document ingestion
- knowledge base
- content governance
Where it fits
Do you need RAG, and on which sources?
Does my need justify RAG, and which sources should go into it?
Lessons in this module
- Choose between RAG, long context, injected data and fine-tuning
- Inventory sources and metadata (this lesson)
What you will learn in the course
This lesson is part of the course Design a RAG architecture that fits your product
- Choose, for an information need, between long context, data injected by the application, RAG and fine-tuning, and justify the choice by volume, update frequency, access rights and cost.
- Specify the source inventory, exclusions, the metadata to capture and the document chunking strategy.
- Design retrieval: semantic, keyword or hybrid search, reranking, contextual retrieval, number of passages and relevance threshold.
- Specify the answer grounded in sources, how citations are displayed, how conflicting sources are handled and what happens when nothing is found.
- Specify access filtering before the model reads any passage, and index freshness (resync, deletions, versions).
- Build a set of 30 test questions that covers RAG failure modes and diagnose which stage caused a wrong answer.
- Estimate the cost items of a RAG system and choose between a hosted tool, a managed knowledge base, a vector database and a custom build.
Related courses
- Build an AI assistant for your productAdvanced · ~3 hr
- Evaluate an AI feature: test sets, metrics and LLM judgesAdvanced · ~3 hr
- Ship and monitor an AI feature in productionExpert · ~3 hr