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Designing reliable AI Systems for Production Environments
What I'm Learning About Enterprise AI Architecture.
1302 words
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7 minutes
Educational AI Systems. What I'm Learning About Governance Gaps
As I explore AI governance through formal coursework and implementation context, I'm identifying specific gaps where educational chatbots introduce operational and compliance challenges that standard enterprise AI discussions often overlook.
2815 words
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14 minutes
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Educational AI at Scale. Why Governance Precedes Capability
What I'm learning about governance in AI education: why decision architecture has to precede technical architecture.
1126 words
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6 minutes
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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.
1740 words
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9 minutes
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Why Document Ranking Matters in RAG. What I'm Learning About Retrieval Failures
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.
1147 words
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6 minutes
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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.
896 words
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4 minutes
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How RAG Actually Works. Building Reliable Enterprise AI with LangChain4j
2026-03-20
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The gap between the architecture diagram and the working system , documented from the inside.
1304 words
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7 minutes
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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.
943 words
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5 minutes
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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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