Software Architecture, Production Systems, and Reliable AI

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 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.

Article 4 of the EU AI Act mandates AI literacy. In EdTech the obligation runs in three directions: staff, students, and parents. Most platforms are meeting none of them adequately.

max_tokens is not a tuning parameter but a reliability control surface. This approach models LLM output as a probabilistic upper bound derived from schema, tokenizer, and model priors. By combining dynamic estimation, constrained decoding, and production observability, it reduces latency variance, prevents verbosity failures, and enables auditable, robust and reliable enterprise AI systems.