What Chunking means in practice
Chunking is the most under-appreciated lever in RAG quality. Fixed-size chunking (the LangChain default of 1000 chars + 200 overlap) is fine for tutorials and terrible for production: it breaks sentences mid-thought, drops the semantic boundaries that make retrieval work, and ignores document structure. Better strategies: semantic chunking that splits on sentence boundaries and merges short ones; section-aware chunking for structured documents with explicit headings; parent-document retrieval that retrieves small chunks but expands the context the LLM sees to the surrounding paragraph or section. Match the chunker to the document type — one chunking strategy across a heterogeneous corpus reliably under-performs a small number of type-specific strategies.
Related terms
Retrieval-Augmented Generation (RAG) →
A pattern where, instead of relying solely on the LLM's training, the system retrieves relevant documents from a knowledge base and includes them in the prompt as context.
Embedding →
A dense numeric vector representation of a piece of text (or image, or audio) such that semantically similar inputs produce vectors close in the embedding space.
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The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.