Production RAG is a data and evaluation system, not just a vector search call. It needs reliable ingestion, hybrid retrieval, reranking, context assembly, citation policy, model selection, and traces that separate retrieval failures from generation failures.
The INFRO view: INFRO lets RAG teams choose and change the generation and utility models without rebuilding provider integrations.
Start with the knowledge contract
Define which sources are authoritative, how quickly updates must appear, who may retrieve each document, and what the answer should do when evidence is missing. These rules determine chunking, indexing, access filters, and freshness jobs.
Store source identity and version with every chunk. Without lineage, citations cannot be trusted and stale answers are hard to debug.
Retrieval is a pipeline
A robust pipeline often combines lexical and semantic search, metadata filters, reranking, deduplication, and diversity control. More chunks do not automatically produce a better answer; they can dilute the evidence and increase input cost.
Measure retrieval recall separately from answer quality. If the required passage never reaches the model, changing the generator cannot fix the problem.
Construct context deliberately
Allocate a context budget by source importance, remove boilerplate, preserve document boundaries, and give the model stable citation identifiers. Include instructions for conflicts and missing evidence.
Use a smaller model for query rewriting or document classification only after it passes a workload-specific evaluation. The retrieval step is often high-volume and a strong cost-optimization candidate.
Observe the full chain
Trace query transformation, retrieved sources, reranker scores, context size, generation route, latency, cost, citations, and user outcome. Privacy rules may require storing identifiers and scores without storing the full source text.
Build an evaluation set with answerable, unanswerable, conflicting, stale, and access-restricted questions. A RAG system is trustworthy when it knows when not to answer.
Where INFRO fits
Query rewriting, reranking assistance, grounded generation, and verification can use different supported models through one INFRO account while request-level usage and cost remain comparable. Routing and failover stay below the retrieval application.
Pair your retrieval evaluation with the INFRO model catalog and quickstart. INFRO does not replace the vector store or data-governance layer; it gives the model side of the RAG architecture one controlled interface.
Frequently asked questions
Is a vector database enough for RAG?
No. Production RAG also needs ingestion, access control, retrieval and reranking policy, context construction, evaluation, citation behavior, and observability.
How do I reduce RAG cost?
Improve retrieval precision, cap context, remove repeated boilerplate, and evaluate efficient models for high-volume rewriting and grounded generation tasks.
Does INFRO store my knowledge base?
INFRO is the model infrastructure layer, not the vector database. Your retrieval system supplies the context to supported models through the INFRO API.
