Prompt Chaining Masterclass: How to Orchestrate Multiple AI Models for Maximum Impact

Prompt chaining is notoriously tricky to learn. I wrote this to make it easy. Unlock the power of prompt chaining—learn real techniques, model-specific strategies, and how to troubleshoot prompts.

Introduction

I remember the days (early in the ChatGPT era) when I’d throw a complex task at a single AI model and hope for magic. Sometimes it worked—often it didn’t. That’s what led me to prompt chaining, a technique where you break a task into stages and let different AI models tackle each part. In simple terms, prompt chaining involves linking multiple AI prompts in sequence so the output of one prompt becomes the input for the next (Prompt Chaining Langchain | IBM) (Harnessing Generative AI: Prompt Chaining & Multi-Model Orchestration). This modular approach shines for complex projects: instead of relying on one model to do everything (and hitting its limits), you orchestrate a team of models, each doing what it’s best at.

Why go multi-model? Think of each AI as a specialist with unique strengths and weaknesses—like coworkers with different expertise. A single model might generate a decent essay, but struggle with verifying facts or writing code. One model might write fluently but get math wrong; another model might be great at logic but terse in language. Chaining lets you overcome the limitations of single-model approaches by handing off tasks between models. It’s a bit like an assembly line: one model outlines an idea, another fills in details, another checks for errors, and so on.

Single-model workflows often hit a wall due to hallucinations, limited context memory, or just running out of steam on complex problems. Recent research in AI suggests that relying on one model can leave accuracy on the table. For example, ensemble methods that combine multiple models have been shown to produce more robust answers, specifically by addressing the shortcomings of any single model ( One LLM is not Enough: Harnessing the Power of Ensemble Learning for Medical Question Answering - PMC ). In my experience, a one-model attempt at a complex problem might give you a plausible-sounding answer that’s completely wrong—and not even wrong in ways that hallucination spot-checking will catch. For instance, if a problem is framed incorrectly, hallucination checking won’t catch that! By contrast, a chained approach—where one model helps construct a good prompt with constraints, another summarizes sources, another cross-checks facts, and a third refines the explanation—yields far more reliable results.

The goal of this masterclass is to share practical, actionable strategies for multi-model prompt chaining. I’ll draw on my own journey (mistakes and all) and the best insights from the AI community. By the end of this article, you’ll know how to:

• Think in tokens – understanding tokenization and why it matters for chaining.

• Know when and why to switch models – spotting the signs of model fatigue or limits.

• Design real-world multi-step workflows – with examples from product specs to deep-dive research.

• Optimize your environment and tools – picking the right setup for your skill level.

• Avoid common pitfalls – and apply advanced strategies to push the envelope.

Whether you’re a developer automating a product document pipeline or a PM building out a new discovery flow or a marketer working on a marketing campaign automation approach, prompt chaining can be a game-changer. Let’s dive in by getting into the mindset that underpins effective chaining: thinking in tokens.