Multi-Step AI Pipelines

Simple automations use one AI step. Powerful automations chain multiple AI steps together, where the output of one feeds the input of the next — like an assembly line of AI workers.

Why Multi-Step?

Single AI calls have limits. They can hallucinate, produce inconsistent formats, or miss nuance. Multi-step pipelines address these problems:

1. 1.Separation of concerns: Each step does one thing well
2. 2.Quality checks: One AI step can verify another's work
3. 3.Specialization: Different prompts (or models) optimized for each task
4. 4.Debuggability: If something goes wrong, you know exactly which step failed

Pipeline Architecture

The Content Production Pipeline

Step 1: Research (Claude Sonnet)

"Research the topic '[topic]' and provide: 5 key points, 3 statistics with sources, 2 expert quotes, and the current state of the debate."

Step 2: Outline (Claude Sonnet)

"Given this research: [Step 1 output], create a blog post outline with: a compelling headline, introduction hook, 5 main sections with subheadings, and a conclusion."

Step 3: Draft (Claude Sonnet)

"Write a 1,500-word blog post following this outline: [Step 2 output]. Use the research: [Step 1 output]. Tone: conversational but authoritative. Include data points to support each section."

Step 4: Edit (Claude Haiku — cheaper, faster)

"Review this draft for: grammar, clarity, factual consistency, tone, and engagement. Suggest 5 specific improvements. Flag any claims that need source verification."

Step 5: SEO Optimize (Claude Haiku)

"Optimize this article for SEO. Add: meta title (under 60 chars), meta description (under 160 chars), suggested URL slug, 5 internal link opportunities, and alt text for 3 suggested images."

Step 6: Human Review

The final draft is sent to a human editor for approval before publishing.

Total time: ~3 minutes of AI processing, ~10 minutes of human review.

Without the pipeline: 4-6 hours of writing.

The Customer Intelligence Pipeline

Step 1: Gather — Collect customer feedback from multiple sources (reviews, tickets, surveys)

Step 2: Clean — AI normalizes format, removes duplicates, fixes typos

Step 3: Classify — AI categorizes each piece of feedback (product, service, pricing, UX)

Step 4: Sentiment — AI scores sentiment (positive/negative/neutral) and urgency

Step 5: Aggregate — AI groups by theme and generates a summary report

Step 6: Recommend — AI suggests the top 3 actions based on the analysis

Step 7: Deliver — Report sent to stakeholders via email/Slack

Choosing the Right Model for Each Step

Not every step needs your most powerful (and expensive) model:

Cost optimization: A pipeline using Haiku for 3 steps and Sonnet for 2 steps costs 60-70% less than using Sonnet for all 5 steps — with minimal quality difference.

Building Pipelines in Practice

In Zapier

Chain multiple "AI Text" actions, passing output from each step as input to the next.

In n8n

Use multiple AI Agent nodes connected in sequence. n8n's visual builder makes the data flow clear.

In Make

Use multiple HTTP modules calling the AI API, with JSON parsing between steps.

Key Principles

1. 1.Test each step independently before connecting them
2. 2.Validate the output format of each step before passing to the next
3. 3.Add logging so you can debug failures
4. 4.Set timeouts — if one step hangs, it shouldn't block everything
5. 5.Build incrementally — start with 2 steps, add more as you validate