Most LLM failures come from poorly written system prompts, not model selection. This guide covers the specific structures (XML tags for Claude, markdown for GPT), token budgeting strategies, and five patterns that consistently work—like making roles absurdly specific, showing exact output schemas instead of describing them, and explicitly telling models when to admit they don't know something. Testing matters too: run your prompts through happy paths, edge cases, and adversarial inputs multiple times before deploying.

The author infers Fable’s core advantage comes from a separate verifier model that checks outputs and curbs errors. This verifier layer likely underpins Fable’s performance lead, measured in months, by reducing hallucinations and accelerating iteration.