RAG Production Checklist
A checklist for taking retrieval-augmented generation from demo to production. It covers ingestion and chunking, document permissions and injection, embeddings and indexes, hybrid search and reranking, grounded answers with citations, retrieval and answer evals, freshness, monitoring and cost, with a severity and a verification step for each check.
7
Sefism · Oct 2026
30
Sefism · Oct 2026
7
Sefism · Oct 2026
AI
Sefism · Oct 2026
Checklist
AI & Machine Learning
Sefism
15 min read
Intermediate
Free
What this checklist is
A checklist for taking retrieval-augmented generation from demo to production. It covers ingestion and chunking, document permissions and injection, embeddings and indexes, hybrid search and reranking, grounded answers with citations, retrieval and answer evals, freshness, monitoring and cost, with a severity and a verification step for each check.
It has 7 chapters and 30 checks, each with how to verify it and how much it matters. For developers shipping retrieval-augmented generation.
Chapter by chapter
1. Data ingestion and chunking (5 checks)
What goes into the index decides what can come out. These checks make sure documents are parsed cleanly and split into chunks that make sense on their own.
2. Access control and document security (5 checks)
Retrieval puts your documents into prompts, so it inherits every permission and trust problem those documents have. These checks are about who can see what, and what a document can make the model do.
3. Embeddings and index choice (4 checks)
The embedding model and the vector index set the ceiling on recall and the floor on latency. Choose both with your own data, then pin them.
4. Metadata, hybrid search and reranking (4 checks)
Pure vector search misses exact terms and ignores structure. Metadata filters, keyword search and a reranker fix most of what users notice.
5. Grounding and citations (4 checks)
Retrieval only helps if the answer actually uses it. These checks keep answers tied to sources users can open and check.
6. Evaluation of retrieval and answers (4 checks)
Evaluate retrieval and generation separately, so you know which half to fix. Then run both on every change.
7. Freshness, monitoring and cost (4 checks)
A RAG system is a data pipeline that never stops. These checks keep the index current, the answers watched and the bill predictable.
How it works on Sefism
The checklist is one page: each check has a severity (must, should or nice to have) and a concrete way to verify it, and members can tick checks off as they go. The opening chapters are open to everyone. Adding it to your account uses credits, opens every chapter and keeps your progress.
Every claim is checked against the sources listed on the page (7 cited).
Ready to start?
Opens on Sefism in a new tab.
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