What Fine-tuning means in practice
Fine-tuning is the process of taking a pretrained foundation model and continuing training on a smaller dataset specific to a domain (clinical, legal, financial) or a task (classification, structured extraction, style transfer). Modern fine-tuning is almost always parameter-efficient — LoRA or QLoRA adapters that train only a fraction of the model weights — which means a customer can host one base model and dozens of domain adapters on the same GPU. For most enterprise use cases retrieval-augmented generation outperforms fine-tuning at lower cost and higher auditability; fine-tuning earns its keep when the customer needs the model to adopt a specific output format, voice, or domain vocabulary that prompting alone cannot reliably enforce.
Related terms
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.
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.
LoRA / QLoRA →
Parameter-efficient fine-tuning that trains a small adapter rather than the full model, allowing many specialised variants to share the same base weights.
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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.