Evidence quality
Parsing, chunk boundaries, metadata, freshness, and retrieval determine what the model can know.
Free Tools / Infrastructure & Architecture
RAG Readiness Checker
Turn your corpus, scale, freshness, permissions, and answer risk into a practical retrieval architecture.
Knowledge source profile
Select everything the assistant must retrieve.
Your RAG architecture will appear here
Profile the corpus, freshness, permissions, languages, and answer risk before seeing recommendations.
Production RAG is more than a vector database
Permission safety
Tenant and user boundaries must constrain candidates before content enters model context.
Evaluation
Relevance, faithfulness, abstention, latency, and cost need versioned test sets and release thresholds.
RAG Architecture Cheat Sheet
Decision trees for chunking, retrieval, and evaluation in production RAG.
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Frequently asked questions
What does RAG mean?+
Retrieval-augmented generation retrieves relevant evidence from your own sources and supplies it to a language model before the model answers.
How accurate is the chunk estimate?+
It is a directional capacity estimate based on pages and source type. Real chunk counts depend on parsing quality, token distribution, tables, code structure, overlap, and deduplication.
Which vector database should I choose?+
Choose only after measuring corpus size, filters, hybrid search, latency, operations, data residency, and cost. A simple pgvector deployment is often enough for smaller systems.
Why do permissions matter before retrieval?+
Filtering after generation is too late. The retriever must exclude unauthorised documents before their content reaches the model context.
Are my selections stored?+
No. The readiness assessment runs entirely in your browser. Framz records only that the verified profile used the tool.
Need the retrieval system built and evaluated?
Framz builds ingestion, hybrid retrieval, permission filtering, evaluation, and operating controls around your actual data.
Design your RAG system