Basic embeddings only get you so far. Ahmad Wael explains the two-stage pattern behind advanced RAG retrieval: a bi-encoder for recall, a cross-encoder for precision, and the caching tricks that keep the second pass off most of your traffic.
Raw correlation makes campaigns look better than they were. Ahmad Wael walks through propensity score matching in Python: logistic regression for the scores, nearest neighbors for the pairs, and what happens to that 13% lift once selection bias is out of the way.
JPEG AI maps images into latent tensors, VVC targets up to half the bitrate of HEVC, and JPEG DNA stores pictures as biochemical sequences. What those standards change for anyone running WordPress at scale, plus a snippet for the new mime types.
RAG fetches the facts; the context layer decides what actually reaches the prompt. Four pieces I use in production: memory with exponential decay, hybrid retrieval with re-ranking, a token budget reserved in order, and extractive compression.
A walkthrough of the VQ-VAE plus transformer pipeline I use for Minecraft world generation, including codebook resets for dead embeddings and weighted cross-entropy for blocks that barely show up in the data.
Models decay in production because the world keeps moving and the training snapshot does not. How data drift and concept drift differ, and the monitoring I use to catch both before a stakeholder does.
Retrieval can work perfectly and still hand the model two documents that contradict each other. A conflict detection layer, the two heuristics that catch most enterprise cases, and cluster-aware recency for resolving them.
Asking a chatbot for a WooCommerce snippet is a lever pull, and the losing pulls are the hard ones to spot. Where LLM generated code breaks WordPress sites, what it does to coding standards, and why per-token pricing makes it worse.
Cowork gets written off as Claude Code for people who don’t code. I use it for architecture work and multi-file debugging, and this is how I keep the context window lean, why I stay in plan mode, and which repeat jobs I turned into skills.
The 512-token split is the default in every RAG tutorial, and it quietly ruins answers on real documents. What I use instead: sentence windows, hierarchical chunking, layout-aware table parsing, and RAGAS to check whether any of it worked.