In the last three years, artificial intelligence models like ChatGPT, Claude, or Gemini have made tremendous strides. Today, they provide answers that seem convincing for almost any query or task. However, that first output is rarely the ideal one. Sticking with it means wasting much of the potential these tools offer.
There is a simple and effective strategy that experts use: chaining several models so that each one refines the work of the previous one. Instead of asking for everything in one step, the task is divided into specialized phases. The final result accumulates successive improvements and comes much closer to being optimal.
When an AI receives a prompt, it generates its response based on the immediate context. If that context is limited or the request is too broad, the output tends to be general and superficial. No professional work is delivered in draft form without revisions, and the same applies here.
Chaining applies that logic to the world of AI. A first AI generates a draft, the second critiques it, the third improves it, and if necessary, a fourth verifies it.
Why specialization works better
Each model responds to the specific role assigned to it at that moment. A prompt that only asks to critique a text is more effective than one that tries to generate and critique at the same time. That specialization is the key to success.
