Stop Guessing Which LLM to Use — Decision Frameworks That Pay for Themselves on the First Choice

The problem: Model selection decisions made on intuition are expensive. The wrong model for an agentic task can cost 10x more, hallucinate more often, and fail specific evaluation criteria that would have been obvious with a framework. Equally, fine-tuning when prompting would have worked is months of wasted effort.

The solution: Five decision frameworks that cover the real choices: which model for which task, when to use RAG vs fine-tuning vs prompting, how to align agent architectures, and how to think about safety across distributed systems.

What you get (5 SKILL.md frameworks):

• Model Selection Guide — the decision matrix for LLM selection by task type and cost profile
• RAG vs Fine-Tuning vs Prompting — when each approach wins, and when it doesn't
• Context Window Optimisation — maximise coherence before the window fills
• AI Alignment Architecture — the alignment patterns that work in production agent systems
• Distributed AI Safety — safety frameworks for multi-agent, multi-node deployments

Who it's for: Operators making architecture decisions on agentic AI systems who want a framework instead of a guess.