Reliable Enterprise AI

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 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 isn’t a single pattern, but a family of architectures. And choosing the wrong architecture isn’t a performance problem, but a design flaw.

The gap between the architecture diagram and the working system , documented from the inside.

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.

In mission-critical environments, an AI that sounds confident but guesses is unacceptable. The difference between a fragile demo and a reliable system lies in engineering, not just clever prompting.

2025 was the year of the realization that AI is incredibly expensive. We’ve been living in an era of mindless model consumption, but the revolution for sustainability has started.