Check whether your sources, access rules, retrieval design, evaluation set and operations are ready for a retrieval-augmented generation project, and see which gaps remain.
What it helps you do
Most delays on RAG projects come from data access, content quality and ownership rather than from the model. Working through this checklist before scoping surfaces those issues while they are still cheap to fix.
How to use it
Tick an item only when it is true today, not when it is planned. Unticked items are your gap list.
Checklist
0 of 19 checked · 19 items still openReading your result
A few open items is normal. Gaps in access control or evaluation should be closed before building; gaps in retrieval design can be resolved during the first iteration.
What to do next
Use the RAG guide in this hub to plan the pipeline, and the AI Project Scoping Template to capture decisions.
If your checklist shows gaps you would like help closing, our RAG Development team can plan the data and retrieval work with you.
RAG Development Services- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksLewis et al., Meta AI · 2020 · Research paperarxiv.org
- Introducing Contextual RetrievalAnthropic · 2024 · Engineering articleanthropic.com
- RAGAS: Automated Evaluation of Retrieval Augmented GenerationEs et al. · 2023 · Research paperarxiv.org