What Context window means in practice
The context window is the maximum number of tokens an LLM can process in one inference call, counting both the prompt and the generated output. Modern open-weights models offer 8K to 128K-token windows, with some research models pushing to 1M+. The practical engineering trap is that quality typically degrades as the context fills — the famous "lost in the middle" effect — so a 128K context is not a free pass to dump every relevant document in. Better-engineered RAG with tight retrieval and re-ranking beats long-context prompt-stuffing on most enterprise workloads, both in answer quality and in inference cost.
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.
Large Language Model (LLM) →
A transformer-architecture neural network trained on very large text corpora to predict the next token in a sequence, producing fluent natural-language output across a wide range of tasks.
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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.