Design Loops, Not Prompts

Building powerful AI now involves designing self-correcting loops rather than just single prompts, but how the AI checks its work is the most critical part. When a model critiques its own output, it often fails to catch its own mistakes, resulting in no improvement over a single guess. A recent experiment revealed a far better method using a deterministic verifier to check if the AI's answer is truly anchored to a source document. This source-anchored approach successfully slashed the hallucination rate by half, proving that reliable AI loops need an external, objective check rather than just internal self-reflection.