The Prompt Chain
The Prompt Chain
Section titled “The Prompt Chain”One-liner: Build a multi-step AI workflow where each step’s output feeds into the next — turning a complex task into a repeatable pipeline.
🔧 Jump in (Tinkerers start here)
Section titled “🔧 Jump in (Tinkerers start here)”Pick a task that has at least 3 distinct phases. Examples: writing a blog post (research, outline, draft, edit), analyzing a dataset (clean, analyze, summarize, recommend), or preparing a presentation (topic research, slide structure, talking points, Q&A prep).
Build a 3-step chain. Each step is a separate prompt. The output of each step becomes the input of the next.
Step 1 — Research/Gather:
You are a research assistant. Your job is to gather the raw material for [your task].
Topic/context: [describe what you’re working on]
Produce a structured collection of: key facts, relevant examples, important considerations, and any constraints. Organize by theme. Do not draft anything — just collect the ingredients.
Copy the output. Start a new prompt (or clearly reset context).
Step 2 — Structure/Draft:
You are a content architect. Your job is to turn raw research into a structured draft.
Here is the research material: [paste Step 1 output]
The final deliverable is: [describe what you need — a blog post, a report, a strategy doc, etc.]
Create a structured draft. Include clear sections, key arguments in order, and placeholders for any examples or data points from the research. Focus on logical flow and completeness.
Copy the output. Start a new prompt.
Step 3 — Polish/Critique:
You are a senior editor. Your job is to make this draft publication-ready.
Here is the draft: [paste Step 2 output]
The audience is: [describe who will read this]
Do three things:
- Improve clarity — simplify any convoluted sentences, cut unnecessary words
- Strengthen weak points — flag any claim that needs better support and add it
- Check consistency — ensure tone, terminology, and formatting are uniform throughout
Produce the final version with an editor’s note listing your key changes.
Now document the chain. Write down the 3 prompts as a reusable template (with [PLACEHOLDERS] for the parts that change). You’ve just built a prompt pipeline.
📋 Plan first (Planners start here)
Section titled “📋 Plan first (Planners start here)”Here’s what you’re about to do:
- Choose a multi-phase task — Something that naturally has distinct stages (research → create → refine). The more phases, the more the chain helps.
- Design the chain — Write 3 prompts, each with a clear role, input expectation, and output format. The key constraint: each step’s output must contain everything the next step needs.
- Run the chain — Execute each step sequentially, passing the output forward. Use fresh contexts between steps to prevent bleed-through.
- Evaluate information flow — Notice where context was lost between steps. What did Step 3 need that Step 2 didn’t preserve?
- Document as a template — Save the chain with placeholders so you can reuse it for the same type of task.
“Done” looks like: A completed deliverable that went through a 3-step pipeline, plus a documented prompt chain template with placeholders for reuse.
🧭 Why this matters (Strategists start here)
Section titled “🧭 Why this matters (Strategists start here)”In WA-Basic-01, you built a single reusable prompt. Here, you’re learning to chain prompts into a workflow — the building block of all production AI automation. Every AI-powered pipeline (content generation, data analysis, document processing) is fundamentally a prompt chain with handoffs. The skill you’re building — decomposing a task into stages, defining clear inputs and outputs, managing context between steps — is the same skill used in tools like n8n, Zapier AI, or custom LLM pipelines. Manual chaining teaches you what to automate and where the bottlenecks live.
Reflection
Section titled “Reflection”- Where did context get lost between steps? What information did a later step need that an earlier step didn’t pass along?
- Did the 3-step chain produce better output than a single “do everything” prompt? Where specifically?
- What surprised you about the output?
- What did you have to fix or override?
- How would you explain what you just did to a colleague?
- 💬 Discuss: Try explaining your result to someone who hasn’t used AI for this task. What questions do they ask? (Social Learners)
⬆️ Level up
Section titled “⬆️ Level up”Ready for more? Try WA-Advanced-01 — where you’ll design and document a complete AI-automated workflow for a business process.
Back to Workflow Automation | 🟡 Intermediate Level