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.
AI hallucinations are not a bug that will be fixed in the next model update. They are a structural consequence of how large language models work. This post explains what hallucination actually is, why it happens at a technical level, the different types, and what practical steps reduce it — without the hype or the panic.
AI benchmarks can't be trusted to choose a model. Discover why MMLU, SWE-bench and others fail in production — and what to measure instead.
RAG pipelines fail at chunking, retrieval, and context assembly — not the LLM. This post covers every key decision in a RAG pipeline and what to choose.
Anthropic released MCP in November 2024. By March 2026 it had 97 million monthly SDK downloads and support from every major AI vendor. This post explains what MCP is, the problem it solves, how it works architecturally, and why it matters for anyone building or integrating AI systems — including in the SAP world.
Temperature, top-p and top-k decide how an LLM picks every word it generates. This post explains what each setting does, how they interact, and what to set for your task.
When one AI agent isn't enough, multi-agent systems take over. This post explains how orchestration, MCP and A2A actually work — and where these systems fail in production.
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