The April 2026 audit identified a different kind of site debt than layout bugs: thin LLM topic coverage, weak sourcing, unclear article status, and no durable way to track improvement. This hub turns that audit into operating pages that editors and AI agents can follow.
8topic familiesFoundations, training, models, applications, tools, safety, evaluation, emerging topics.
20first-batch pagesTransformer, RLHF, GPT-4, hallucination, privacy, evaluation, and related core entries.
6review dimensionsCompleteness, sourcing, neutrality, structure, freshness, and internal linking.
4implementation pagesStandards, backlog, templates, and metrics dashboard.
Audit inventory
| Category | Representative pages | Main issue | Priority | Implementation route |
|---|---|---|---|---|
| Architectures and NLP basics | Transformer architecture, self-attention, tokenization | Stub or minimal explanations, weak diagrams, sparse references. | High | Batch backlog |
| Pretraining and tuning | Pre-training, fine-tuning, RLHF | Outdated structure and missing comparison language. | High | Training templates |
| Major models | GPT-4, LLaMA, BERT family | Model records need release status, source dates, capabilities, and limitations. | High | Model registry |
| Applications | Chatbots, summarization, code generation | Thin coverage and not enough practical examples. | High | Batch backlog |
| Tools and infrastructure | Hugging Face, LangChain, vector databases | Useful but thin framework context. | Medium | Source map |
| Ethics, safety, and evaluation | Hallucination, bias, privacy, evaluation metrics | Needs careful sourcing, neutral tone, and warning labels. | High | Editorial standards |
Editorial Standards
Neutral tone, citation requirements, quality grades, source preference, and review states.
High-Priority Article Backlog
The 20-page first batch from the audit, grouped by topic family and edit goal.
Article Templates
Reusable outlines for concepts, models, applications, tools, safety topics, and evaluations.
Metrics Dashboard
Stub debt, citation coverage, stale reviews, and QA checks ready for manual or scripted tracking.
Implementation posture
- Classify first. Every LLM topic page gets a topic family, quality class, source state, and last-reviewed date before expansion work starts.
- Expand high-impact stubs. The first batch focuses on core concepts readers expect to be substantial: Transformer, self-attention, tokenization, RLHF, GPT-4, LLaMA, hallucination, privacy, and evaluation.
- Require sources near claims. Factual or time-sensitive statements need an official, primary, or peer-reviewed source before they are treated as reviewed content.
- Track improvement. Pages move through Stub, Start, C, B, A, and Reviewed states only when evidence and structure justify the change.