What is Reliable Enterprise AI? The Complete Architecture Guide
Discover what Reliable Enterprise AI truly means. Explore the definitive architectural guide for moving beyond naive chatbots to deterministic, secure, and production-grade RAG systems.
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Latest Posts
Hybrid Search in Enterprise RAG. Why Alpha Tuning Is an Architectural Decision
Pure vector search fails silently on exact identifiers. BM25 misses semantic paraphrases. In enterprise RAG, combining both signals isn't a performance trick — it's the minimum architecture for reliable retrieval under EU AI Act compliance. Here's what actually breaks in production, and how to fix it.
Naive RAG Fails When Documents Are Ranked Wrong
Naive RAG retrieval relies on vector similarity alone. This fails silently when documents are semantically similar but contextually irrelevant. Re-ranking adds a second filtering gate that evaluates actual relevance. This improves accuracy 16%, costs 14x latency, and creates an auditable decision trail required by the EU AI Act.
RAG Is Not One Thing. A Practical Architecture Map for Reliable Enterprise AI Systems
RAG isn’t a single pattern, but a family of architectures. And choosing the wrong architecture isn’t a performance problem, but a design flaw.
How RAG Actually Works. Building Reliable Enterprise AI with LangChain4j
The gap between the architecture diagram and the working system , documented from the inside.
Reliable Enterprise AI. What Enterprise Architects Must Understand About Transformers
Underneath all the layers of modern AI sits a single architectural breakthrough, the Transformer. Understanding it is not an academic curiosity; it's a requirement for designing reliable systems.
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Raúl Ferrer
Software Architect studying Reliable Enterprise AI systems. I document RAG architecture patterns, retrieval failure modes, and EU AI Act compliance requirements for production environments. Author of iOS Architecture Patterns (Apress).
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