Enterprise RAG at Scale: The Complete Engineering Guide
A practical, end-to-end series for building multilingual, secure, measurable retrieval-augmented generation systems that survive production traffic.
Enterprise RAG
8 articles tagged “Enterprise RAG”.
Building Production RAG: Reference Architecture and Rollout Playbook
Assemble ingestion, hybrid retrieval, reranking, grounded generation, evaluation, and operations into a deployable API with a staged enterprise rollout.
Operating Enterprise RAG: Security, Observability, SLOs, and Cost
Secure and operate RAG with retrieval-time authorization, prompt-injection defenses, trace-level observability, freshness SLOs, cost controls, and incident playbooks.
Evaluating RAG: Metrics, Golden Sets, Experiments, and Results
Measure retrieval, ranking, grounding, answer quality, latency, and cost with reproducible datasets, human review, statistical tests, and realistic benchmark reports.
Vector Indexes for RAG: HNSW, IVF, Quantization, and Scale
Choose, tune, shard, and migrate vector indexes using recall-latency measurements, realistic filters, memory estimates, and production capacity tests.
RAG Ranking: Rerankers, Context Packing, Citations, and Abstention
Turn a high-recall candidate pool into compact evidence using reranking, deduplication, diversity, token budgets, citation validation, and no-answer policies.
RAG Retrieval: Query Rewriting, Hybrid Search, and Fusion
Design the online retrieval path with language and intent detection, safe rewrites, BM25 plus vector search, filtering, RRF fusion, and candidate budgets.
RAG Ingestion: Multilingual Data, Chunking, Metadata, and Freshness
Build a replayable ingestion pipeline with language detection, OCR, permission-aware chunks, embedding versioning, and measurable data quality.