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.
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.
A 2026 roadmap for learning Python for data science without the tutorial detours: which fundamentals actually matter, the three libraries you will live in, and how much time to give data structures before interviews.
Connecting Claude to a WordPress site over the Model Context Protocol turns your posts and comments into something it can read. Here is the setup, the gap-analysis prompt I use, and how I score the ideas that come back.