SAP Business Data Cloud unifies fragmented SAP data into a governed semantic layer for analytics and AI. Understand the architecture, components, and what it means for your SAP roadmap.
Context engineering is the layer prompt engineering can't cover. Learn what it is, why it matters for production AI, and how to apply it — with SAP examples.
YAML is the dominant format for config files — Kubernetes, Docker Compose, GitHub Actions and more. This post explains how YAML works and when to choose it over JSON.
LLMs don't truly remember. This post explains how context windows work, why AI forgets between sessions, and the four memory types that real AI systems use to work around it.
AI regulation looks different everywhere, but the logic is identical. This post explains the risk-based model behind the EU AI Act and why regulators worldwide are copying it.
This post explains how generative AI works — tokens, embeddings, the transformer and self-attention. The mechanics behind every LLM, explained without a single equation.
Fine-tuning, prompt engineering and RAG each solve a different problem. Pick the wrong one and you waste time and money. This post explains what each does and when to use it.
78% of organisations use AI. But what are they actually doing? This post maps real enterprise AI in 2026 — copilots, code generation, knowledge search, process automation and agents — where it works and where it fails.
RAG is the most important technique for making AI reliable in enterprise settings. It gives an LLM access to your documents, data and knowledge at query time — so it answers from evidence, not from memory. This post explains what RAG is, how it works step by step, where vector databases fit in, and how SAP uses it in Joule and AI Core.
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