LLM systems consume data from across the enterprise — data governance frameworks that track lineage, enforce consent, and handle deletion requests across AI pipelines are now regulatory requirements, not nice-to-haves.

LLM outputs exhibit differential behavior across demographic groups in ways that are hard to detect and harder to fix — bias detection strategies for enterprise AI systems that affect real decisions.

AI audit trails are legally required in regulated industries and increasingly expected everywhere — here's what to log, how to architect compliant log storage, and how to redact PII without losing forensic value.

Banks have 30 years of model risk management practice — adapting SR 11-7 and Basel IV model risk principles to LLM systems gives regulated industries a compliance-ready framework for AI governance.

The EU AI Act's general provisions became enforceable August 2, 2026 — this is what 'high-risk AI' actually means for engineering teams, what documentation is required, and what the Digital Omnibus deadline shift changes.

Basic RAG works in demos — production RAG requires query expansion, semantic caching, routing, fallback strategies, and monitoring patterns that the tutorials don't cover.

Retrieval quality is the most common RAG failure point and the hardest to measure — RAGAS provides the metrics framework, but building an eval pipeline that catches regressions before they hit production requires engineering work.

Shared vector stores across tenants leak data unless access control is enforced at the chunk level — the filtering strategies and architectural patterns that prevent cross-tenant retrieval in enterprise RAG.

Chunks lose their context when split from source documents — Anthropic's contextual retrieval pattern injects document-level context into each chunk before embedding, reducing retrieval failures by 35-49%.

GraphRAG adds a knowledge graph extraction layer over your corpus to answer cross-document questions that flat vector retrieval misses — but it costs 10-100x more to build and query, so the use case must justify it.