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The Prompt Chain

From GW AI Fluency Wiki

Build a multi-step AI workflow where each step's output feeds into the next. 25 minutes.

One-liner: Build a multi-step AI workflow where each step's output feeds into the next β€” turning a complex task into a repeatable pipeline.


πŸ”§ Jump in (Tinkerers start here)[edit | edit source]

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 β€” 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 β€” just collect the ingredients.

Copy the output. Start a new prompt (or clearly reset context).

Step 2 β€” 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 β€” 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 β€” 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: 1. Improve clarity β€” simplify any convoluted sentences, cut unnecessary words 2. Strengthen weak points β€” flag any claim that needs better support and add it 3. Check consistency β€” 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.


πŸ“‹ Plan first (Planners start here)[edit | edit source]

Here's what you're about to do:

  1. Choose a multi-phase task β€” Something that naturally has distinct stages (research β†’ create β†’ refine). The more phases, the more the chain helps.
  2. Design the chain β€” 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.
  3. Run the chain β€” Execute each step sequentially, passing the output forward. Use fresh contexts between steps to prevent bleed-through.
  4. Evaluate information flow β€” Notice where context was lost between steps. What did Step 3 need that Step 2 didn't preserve?
  5. Document as a template β€” 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)[edit | edit source]

In WA-Basic-01, you built a single reusable prompt. Here, you're learning to chain prompts into a workflow β€” 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 β€” decomposing a task into stages, defining clear inputs and outputs, managing context between steps β€” 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[edit | edit source]

  • 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 was the improvement?
  • Which step in the chain was the weakest link? How would you redesign it?
  • πŸ’¬ Have a colleague run your documented chain on a different task of the same type. Their experience reveals whether your chain is truly reusable or depends on your implicit knowledge. (Social Learners)

⬆️ Level up[edit | edit source]

Ready for more? Try WA-Advanced-01 β€” where you'll design and document a complete AI-automated workflow for a business process.

Back to Workflow Automation