Prompt caching, batching and model routing are the real levers for cutting LLM cost and latency in production — not a single silver-bullet trick to rely on blindly.
Learn the four reasoning patterns behind AI agents — ReAct, Planning, Reflection and Multi-Agent. What each one does, when to use it, and when it breaks in production.
Every RAG system runs on a vector database. This post explains embeddings, semantic search and cosine similarity — no prior knowledge of AI or databases required.
Multimodal AI processes text, images, audio and video in one model. This post explains how it works, why native multimodality matters and where it is already in use.
Not all AI acts on its own. This post explains the autonomy spectrum — from reactive assistants to fully autonomous agents — and where human oversight fits in between.
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.
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.
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