Enterprise AutoRAG: measuring quality, cost, and governance
Red Hat proposes treating RAG optimization as a measurable discipline rather than a series of intuitive adjustments. For Belgian and French organizations, the value lies as much in the resulting evidence as in the technology: retrieval quality, answer faithfulness, cost, access rights, and document drift must be monitored together.
1. A three-pipeline architecture
In its September 22 publication, Red Hat separates indexing, real-time retrieval, and continuous maintenance. Indexing transforms documents, preserves their structure, chunks them, and produces embeddings. Retrieval combines hybrid search, reranking, access control, prompt construction, and generation. Maintenance handles updates, deletions, erasure requests, reindexing, and corpus drift.
This separation addresses a common failure: blindly expanding retrieved chunks or context can increase latency, GPU memory, and cost without improving the answer. AutoRAG, offered in OpenShift AI 3.5, automates testing combinations of parsing, chunking, embeddings, search, reranking, and models so teams can compare quality, cost, and latency.
2. What this changes for a Belgian or French company
An SME or mid-market company can replace informal validation with representative questions, expected answers, and thresholds before production. A large enterprise or public administration can require the same protocol across business units and vendors, then retain configurations, scores, and decisions as evidence. The announcement does not, however, remove the need for risk assessment or control over data and subprocessors.
For an assistant connected to Odoo, the corpus must reflect actual permissions: a user should not retrieve a contract, invoice, or HR record they cannot open in the ERP. Testing must therefore cover not only accuracy, but also cross-company leakage, data freshness, and behavior when sources conflict.
3. Underside analysis: no single metric is sufficient
Red Hat separates retrieved-context correctness, answer faithfulness to that context, and final-answer correctness. This matters because a model can faithfully answer from passages that do not contain the right facts. A strong faithfulness score therefore does not prove overall quality. Test sets should cover critical failures, French-Dutch-English cases, and complex documents found in Belgian or French operations.
Using an LLM as judge creates another dependency. Teams should version the judge, monitor drift, and have humans review a sample. For local, private, or hybrid AI, sovereignty also depends on vector-store and log locations, control of keys, exportable results, model portability, and the ability to rebuild the index.
4. An operational governance cycle
Start by identifying which decisions the system can influence and which errors are unacceptable. Build an evaluation set from business cases, with verifiable sources and answers. Compare configurations under accuracy, latency, and cost constraints; test permissions and deletion separately. Then version the corpus, parser, embeddings, model, prompts, and thresholds. Any material change to a document, model, or permission should trigger targeted reassessment.
Immediate priority: before connecting a RAG pilot to Odoo or a document repository, define a reference set, three complementary metrics, a latency and cost budget, and a clear event that triggers reindexing and reassessment.
Design governed RAG