The Art of Chaining AI Models for Complex Tasks
Last month I spent three days debugging a pipeline where GPT-4o was summarizing legal documents, Claude was extracting structured clauses, and a fine-tuned Mistral was classifying risk. The output was spectacular. The orchestration was a disaster — race conditions, token overflow on long contracts, and a silent hallucination that slipped through because no model in the chain was responsible for catching another model's errors. I fixed it. Then I started thinking hard about why chaining AI models is still so poorly understood, even by people who do it every day.
Why Single-Model Thinking Is Holding You Back
Most developers reach for one model and try to get it to do everything. Write the code, test the code, review the code, explain the code. It's the path of least resistance, and it works — until it doesn't.
The problem is that general-purpose models are trained to be generalists. They're optimized for breadth. When you need depth — structured JSON extraction from ambiguous text, high-recall retrieval across 10,000 chunks, or deterministic classification — a generalist model will give you generalist results. It'll hallucinate field names. It'll round trip on retrieval. It'll confidently misclassify edge cases.