Designing reliable AI Systems for Production Environments
What I'm Learning About Enterprise AI Architecture.
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Latest Posts
Prompt Injection in Educational AI. The Security Risk Hidden in Your Compliance Gap
A prompt injection attack on an educational AI system is not just a security incident. Under EU AI Act Article 9, it is a risk your management system should have anticipated, documented, and mitigated before deployment.
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 & Tech Lead. Applying software and systems engineering principles in production to build reliable, observable, and maintainable AI. Author of iOS Architecture Patterns (Apress).
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