Chaining AI models

We keep hearing about chaining models. The idea is simple: connect more than one artificial intelligence (AI) system so each does what it’s best at. One model parses text. Another pulls context. A third writes the answer. None of them is magical alone. Together, they look smarter.

One job at a time

Each model is good at one task. She reads, she searches, she generates. Hand off the work in a sequence and each stays in her lane. That’s easier to trust than betting everything on one giant black box.

Passing the baton

Think of it like a relay race. The output of one becomes the input of the next. If one stumbles, the chain breaks. So we keep the steps clear, predictable, and easy to test. Fewer surprises later.

LangChain in practice

LangChain makes this setup less painful. It’s a framework that knows how to pass text around between models, APIs, and data stores. She handles prompts, memory, and orchestration. We don’t reinvent the wheel; we just wire the pieces with her help.

Simple chains first

Start with two or three steps. For example, take a user’s question, fetch documents, then summarize. If that works, add more. Don’t build a Rube Goldberg machine. Each new link is another chance to fail.

When it works

Chaining feels like teaching a group project where no one hogs all the work. She handles what she’s good at, then moves it along. Done right, the result feels smooth, almost obvious.

We like to imagine ourselves as the project manager. The models do the heavy lifting. Our job is to keep them from tripping over each other. Simple enough.