A scoring layer that measures attribution and specificity, so you can catch confident hallucinations instead of trusting responses that just sound right. Includes the Python logic and a regression suite for CI.
Flat RAG breaks on complex documents like contracts and research papers because it flattens away their structure. Proxy-Pointer RAG uses structure-aware retrieval and breadcrumb embeddings to keep that hierarchy intact when comparing them.
OpenAI’s MRC protocol runs their 131,000-GPU training fabric by disabling BGP and OSPF and pushing routing intelligence to the endpoints. A look at how “dumb” switches and a lossy Ethernet design cut the tail latency that stalls massive GPU clusters.
Vibe checks do not scale. Here is how to score a WordPress AI agent on five measurable dimensions, accuracy, reliability, latency, cost, and business impact, using golden datasets and the LLM-as-a-judge pattern to move from fragile demos to production.
Instructions alone will not give a model an identity. Three ways to train one persona into a small model, and why first-person statements generalized better than chat demonstrations or factual documents.
How to move past vibe coding to a spec-driven agentic AI workflow for production WordPress: persistent specifications, guardrails against silent failures, and automated tests, using tools like CodeSpeak and the Claude API.
Why AI performance problems usually trace back to inference architecture, not the model: retrieval quality, context compression, compute routing, and speculative decoding, with fine-tuning often a distraction.
A practical workflow for Claude Code: use Plan Mode before it touches your code, keep a CLAUDE.md skill file so fixes stick, and keep the context lean. The goal is production-ready WordPress, not AI-generated technical debt.
The WooCommerce integration for Claude adds 18 prebuilt AI workflows that behave like a senior analyst inside your store, from revenue triage to inventory risk reviews. They run as slash commands over live data, not static charts.
Semantic search alone misses exact technical terms. Combining BM25 with dense vectors, then re-ranking the top results with a cross-encoder, is what made our production RAG accurate. RAGAS metrics show the difference.