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.
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.
Prompting stops scaling after a few sessions. Here is how agent skills put your recurring logic into a SKILL.md file instead, with a weekly visualization example and the directory layout I use.
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.
One working directory holds one state of the project, which is why you and your agent keep colliding. Git worktrees give each branch its own folder, and here is the script that pays the setup tax: env files, installs and a unique port offset.