--- name: model-sovereignty-b67f8298c939 description: This skill should be used when the user asks about "local models", "custom models", "fine-tuning", "self-hosting models", "model selection", "which model should I use", "data privacy and models", "LoRA", "RAG vs fine-tuning", "Ollama", "vLLM", or wants guidance on whether to build, host, or customise their own AI models. --- # Model Sovereignty Model sovereignty is the practice of making deliberate decisions about which models to use, where they run, and whether to create custom models. It extends the framework's Theme #2 (Agency and Sovereignty) into the model layer. This skill guides practitioners through the decision framework from cross-cutting Theme #17 and Appendix P of the framework. ## The Decision Hierarchy Exhaust simpler approaches before escalating complexity. Each step adds maintenance burden. 1. **Prompting + context engineering** — the default. Most teams underestimate how far this carries them. Exhaust it first. 2. **RAG** — when the limitation is knowledge (volatile, large, or frequently changing information). 3. **Fine-tuning (LoRA/QLoRA)** — when the limitation is behaviour (consistent domain-specific patterns at scale). 4. **Distillation** — when the limitation is size or speed (edge deployment, latency-sensitive applications). 5. **Local hosting** — when the limitation is privacy, cost at scale, or independence from vendor defaults. ## The Decision Framework Walk through these questions in order. Stop at the first "yes." **Does your data require local processing?** PII, regulated data, trade secrets, or data subject to residency requirements → local hosting is non-negotiable for those interactions. Consult `references/decision-framework.md`. **Does knowledge change frequently?** Information changing weekly/monthly → add a RAG layer regardless of hosting choice. RAG updates instantly; fine-tuning requires retraining. **Does the model need consistent domain behaviour at scale?** Reliable format compliance, style consistency, or decision logic across thousands of requests → fine-tune with LoRA/QLoRA. Consult `references/technique-comparison.md`. **Is baseline load above the break-even threshold?** ~30M tokens/day sustained → self-hosted inference is economically justified within 4 months. Consult `references/hosting-options.md`. **None of the above?** Use cloud API models with good prompting and context engineering. ## The Sovereignty Test Ask: "If my API provider changed pricing, rate-limited me, or discontinued my model tomorrow, what would happen?" A sovereign engineer has an answer: - A fallback model identified and tested - Specifications precise enough to regenerate with any capable model - Local alternatives evaluated for critical workflows - Data classification that determines what can route where ## Getting Started **For data sovereignty:** Start with data classification. List every type of data that flows through your AI interactions. Classify each as Public, Internal, Sensitive, or Restricted. Update MODEL_ROUTING.md with routing rules based on classification. **For cost sovereignty:** Calculate your monthly token usage. Compare API costs against self-hosted alternatives at your volume. The break-even is typically 30M tokens/day sustained. **For domain sovereignty:** Identify your three most common AI failure modes. If failures come from missing knowledge → evaluate RAG. If failures come from inconsistent behaviour → evaluate fine-tuning. If failures come from reasoning capability → stay on frontier APIs with better prompting. **For operational sovereignty:** Identify your vendor dependency. Could you switch providers in a week? A month? Never? The answer determines your urgency. ## Maintenance Awareness Custom models accumulate maintenance debt. Budget for: - Model versioning (pin versions, test before updating) - Retraining cadence (quarterly for fine-tuned models) - Drift detection (monitor output quality metrics) - Exit strategy (every custom model should have a fallback) For detailed technique comparisons, hosting option evaluation, and current-era model recommendations, consult the reference files. --- Source attribution: Habitat-Thinking Original project: https://github.com/Habitat-Thinking/ai-literacy-superpowers/blob/a50b8136e8e170a828b7a61aa3e8815475b56bc9/ai-literacy-superpowers/skills/model-sovereignty/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: Habitat-Thinking Original project: https://github.com/Habitat-Thinking/ai-literacy-superpowers/blob/a50b8136e8e170a828b7a61aa3e8815475b56bc9/ai-literacy-superpowers/skills/model-sovereignty/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Data Scientist, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: API Tools: Ollama, Vllm Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: e85cb3d84e7e879f55df254cf89461a186c79fd5dd12151f60f66619a96e0f41