Checklist · · 12 checks
RAG production-readiness checklist
Twelve checks to pass before a retrieval system answers a real user. Tick them locally; progress stays in your browser.
Topic
RAG
24 entries and 1 reference.
In the glossary: Retrieval-augmented generation (RAG), what each means and how to say it in a review
Part of Data & Databases: all Data & Databases entries · the Data & Databases lens on the map
Entries
Failure story · · 1 min read
The retrieval cache that served stale policies
A well-meaning cache in front of retrieval kept answering from last quarter's HR policy for eleven days.
Deep dive · · 6 min read
Production RAG does not fail loudly, and that is the whole problem
Six days of notes on operating retrieval systems after launch, where nearly every real failure arrives dressed as a good answer.
Deep dive · · 6 min read
Most RAG failures are faithful answers to bad evidence
Six posts on reranking, citations and decomposition, and the uncomfortable thing they turn out to have in common.
Deep dive · · 7 min read
No model can reason about evidence your retriever never returned
Six days of retrieval notes, and not one of the failures announced itself.
Explainer · · 2 min read
Chunk for the question, not for the token limit
Chunk size is a decision about what a complete answer looks like. Reranking, context budgets and refusal thresholds finish the job.
Explainer · · 2 min read
Test retrieval on its own or you will tune the wrong thing
Most "the model is wrong" problems are recall problems. Hybrid search, query rewriting and versioned indexes are where they get fixed.
Explainer · · 2 min read
Ingestion is a data product, not a loader script
Parsing sets your quality ceiling, failures need a destination, and the update and delete paths are the half nobody tests.
Explainer · · 2 min read
Your vector index does not know who is allowed to read it
Embeddings carry no access control lists. Ownership, permissions, freshness and lineage have to be designed before anything is indexed.
Deep dive · · 6 min read
Everything that makes an AI demo impressive is a constraint you have not added yet
Six posts on the distance between a system that answers well once and a system people are willing to depend on.
Explainer · · 1 min read
Why RAG must be evaluated in parts
Why RAG evaluation has to measure retrieval, grounding, generation, and citations separately.
Explainer · · 2 min read
Why weak evidence should trigger recovery, not confidence
Why weak evidence should trigger recovery, not a fluent answer with false confidence.
Explainer · · 1 min read
Why reflection only matters when tied to evidence
Why reflection only helps when it checks the answer against evidence and changes behavior.
Explainer · · 2 min read
Why freshness is part of correctness
Why freshness becomes part of correctness when the world changes faster than your index.
Explainer · · 2 min read
Why evidence is becoming multimodal
Why evidence quality changes when the source is visual, tabular, audio, or multimodal.
Explainer · · 2 min read
Why production knowledge often lives in systems, not PDFs
Why production knowledge often lives in systems of record, not only in documents.
Explainer · · 2 min read
Why some answers live in relationships, not documents
Why some answers depend on relationships that plain document search can miss.
Explainer · · 2 min read
Why one user question may need many searches
Why one user question may need multiple searches before the evidence is good enough.
Explainer · · 2 min read
Why complex AI questions need decomposition
Why complex questions become more reliable when the system answers them in parts.
Explainer · · 2 min read
Why RAG needs both broad context and precise evidence
Why good RAG often needs both precise snippets and enough surrounding context.
Explainer · · 2 min read
Why embeddings are useful and easy to overtrust
Why semantic similarity is useful, but not the same as correctness or trust.
Explainer · · 2 min read
Why chunk boundaries shape answer quality
How chunk boundaries decide what evidence the system can actually retrieve.
Explainer · · 2 min read
Why retrieval quality starts before search
Why answer quality starts when knowledge enters the system, not when the user asks.
Explainer · · 2 min read
Why RAG is an evidence design problem, not a buzzword
Why RAG is really about trusted evidence, not just letting the model search.
References
- Read
RAG at Scale: Architecture and Lessons
Google Cloud · Deep dive · A production-oriented walkthrough of retrieval architecture choices and their operational cost.