Prompt Chaining: How to Build Multi-Step AI Workflows Like a Pro

15 min read By FindSkill Team

Build powerful multi-step AI workflows with prompt chaining. Get copy-paste templates, real examples, and learn how to avoid the mistakes most people make.

Table of Contents

You’ve probably hit this wall before: you ask your AI assistant to do something complex, and it either gives you a half-baked answer or goes off on a tangent. The problem? You’re trying to cram too much into a single prompt.

That’s where prompt chaining comes in. Instead of writing one mega-prompt that tries to do everything, you break your task into steps where each prompt builds on the last one’s output. Think of it like cooking—you don’t throw all ingredients in at once and hope for the best. You follow a recipe, step by step.

In this guide, I’ll show you exactly how prompt chaining works, when to use it, and give you five ready-to-use workflow templates you can copy right now.

What Is Prompt Chaining?

Prompt chaining is the practice of breaking down complex tasks into a sequence of simpler prompts, where each step uses the output from the previous one as input.

Instead of:

  • One giant prompt asking for everything at once
  • Hoping the AI doesn’t forget your requirements halfway through

You get:

  • Clear, focused prompts for each step
  • Better quality output because the AI focuses on one thing at a time
  • More control over the process
  • Ability to validate and adjust between steps

Real-world analogy: Writing a business plan isn’t one task—it’s market research, then competitive analysis, then financial projections, then putting it all together. Each step needs the previous step’s output. That’s prompt chaining.

Simple Example: 2-Step Content Chain

Let’s start with something basic. Say you want to turn a blog post idea into a polished article.

Step 1: Outline Generation

I want to write a blog post about "How remote work affects mental health."

Create a detailed outline with:
- 5-7 main sections
- 2-3 subsections under each
- Suggested word count per section
- Key points to cover

Target audience: Remote workers aged 25-40
Tone: Supportive and practical
Length: 1,500 words total

Why this works: You’re getting structure first. The AI focuses purely on organizing ideas logically.

Step 2: Write the Article

Using this outline:
[paste the outline from Step 1]

Write the complete blog post. Follow the structure exactly and hit the word count targets. Use conversational language, include specific examples, and end each section with a practical takeaway.

Why this works: Now the AI has a roadmap. It’s not making structural decisions AND writing at the same time. The output is way more coherent.

What you just did: Created a 2-step chain where outline → full article. Each prompt has one clear job.

Complex Example: 5-Step Research & Analysis Workflow

Here’s where chaining gets powerful. Let’s say you’re analyzing whether to enter a new market.

Step 1: Information Gathering

I'm considering launching a project management tool for remote design teams.

Research and summarize:
1. Current market size and growth rate
2. Top 5 competitors and their pricing
3. Main pain points design teams mention in reviews
4. Emerging trends in this space

Format as bullet points with sources where possible.

Step 2: Competitive Analysis

Based on this market research:
[paste Step 1 output]

Create a competitive analysis table comparing the top 5 competitors across:
- Core features
- Pricing tiers
- Target customer size
- Unique selling points
- Weaknesses/gaps

Then identify 3 opportunities where competitors are underserving the market.

Step 3: Feature Prioritization

Given these market opportunities:
[paste the 3 opportunities from Step 2]

And these competitor weaknesses:
[paste weaknesses from Step 2]

Propose 8-10 features for our MVP that:
- Address the identified gaps
- Are buildable in 3-4 months
- Create clear differentiation

Rank them by impact vs. effort.

Step 4: Pricing Strategy

Based on this competitive pricing data:
[paste pricing section from Step 2]

And our proposed features:
[paste Step 3 output]

Recommend a pricing strategy including:
- 3 pricing tiers with feature breakdown
- Price points for each tier
- Justification based on competitor positioning
- Expected conversion rates per tier

Step 5: Go/No-Go Summary

Synthesize all previous analysis:

Market research: [paste Step 1]
Competitive gaps: [paste opportunities from Step 2]
Proposed features: [paste top 5 from Step 3]
Pricing strategy: [paste Step 4]

Create an executive summary (300 words max) with:
- Clear recommendation (Go/No-Go/Pivot)
- Top 3 reasons supporting the recommendation
- Biggest risk and mitigation strategy
- Next immediate actions if we proceed

What just happened: You guided the AI through a complete strategic analysis. Each step built on the last, and you could have adjusted course at any point if something looked off.

Why this beats a single prompt: Try asking “Should I launch a PM tool for designers?” in one shot. You’ll get a generic answer. This chain gave you research → analysis → strategy → decision with receipts.

When to Chain vs. Use a Single Prompt

Use a single prompt when:

  • The task is straightforward (“Write a product description for…”)
  • You need one type of output (“List 10 blog post ideas”)
  • There’s no logical sequence of steps
  • Speed matters more than depth

Use prompt chaining when:

  • The task has natural stages (research → analysis → action)
  • You need to validate output before proceeding
  • Quality matters more than speed
  • You’re building something complex (reports, strategies, content series)
  • Early steps inform later decisions

Quick test: If you find yourself writing “first do X, then do Y, then do Z” in a single prompt, you probably need chaining.

Cost Considerations: When Chaining Gets Expensive

Here’s the catch nobody talks about: prompt chaining uses more API calls. If you’re using Claude, GPT-4, or other paid AI APIs, chains can get pricey fast.

Example cost breakdown:

Single mega-prompt:

  • 1 API call
  • ~3,000 tokens input + ~1,500 tokens output
  • Cost: ~$0.15 (GPT-4 pricing)

5-step chain:

  • 5 API calls
  • Total: ~4,000 input tokens + ~3,000 output tokens (cumulative)
  • Cost: ~$0.35 (GPT-4 pricing)

The math changes with scale: Running one analysis? Not a big deal. Running 100 per day? That’s $20/day vs. $35/day. Over a month: $600 vs. $1,050.

Cost optimization strategies:

  1. Use smaller models for simple steps - GPT-3.5 or Claude Haiku for outlining, GPT-4 for final output
  2. Cache repeated context - If you’re using the same system prompt across chains, many APIs now support caching
  3. Validate before continuing - Don’t run all 5 steps if step 1 failed
  4. Batch when possible - Process multiple items in one step instead of separate chains
  5. Know when NOT to chain - Sometimes a single well-crafted prompt is actually better

Real talk: Chaining is powerful but not free. Budget accordingly.

Tools for Prompt Chaining

You don’t need fancy software to chain prompts—you can do it manually. But tools help when you’re running chains repeatedly or building complex workflows.

Manual Chaining (Free)

Just copy-paste between prompts in ChatGPT, Claude, or any AI chat. Works great for one-off tasks.

Pros: Zero cost, maximum flexibility, learn the mechanics Cons: Tedious for repetitive workflows, easy to lose track of context

LangChain (Developer Tool)

Python/JavaScript framework for building AI chains programmatically.

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Step 1: Generate outline
outline_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate(
        input_variables=["topic"],
        template="Create an outline for {topic}"
    )
)

# Step 2: Write content
content_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate(
        input_variables=["outline"],
        template="Write article based on: {outline}"
    )
)

# Run the chain
outline = outline_chain.run(topic="AI workflows")
article = content_chain.run(outline=outline)

Pros: Automate repetitive chains, integrate with apps, version control Cons: Requires coding knowledge, setup overhead

No-Code Options

Make.com / Zapier: Connect AI APIs with other tools. Example: Google Form submission → Claude analyzes response → sends summary email → saves to Airtable.

Relevance AI: Build AI chains with visual interface. Drag-and-drop prompt steps.

Dust.tt: Workflow builder specifically for AI chains. Free tier available.

Pros: No coding needed, visual workflow builders, integrate with other tools Cons: Monthly cost, learning curve, less flexibility than code

My recommendation: Start manual. Once you find yourself running the same chain 10+ times, automate it.

5 Ready-to-Use Workflow Templates

Copy these and customize for your needs.

Template 1: Content Repurposing Chain

Use case: Turn one blog post into multiple content pieces

Step 1: Extract key points
"From this article: [paste article]
Extract the 7 most valuable insights as standalone points. Each should be self-contained."

Step 2: Create tweet thread
"Using these insights: [paste Step 1]
Write a 10-tweet thread. Start with a hook, end with a CTA to read the full article."

Step 3: Generate LinkedIn post
"Using these insights: [paste Step 1]
Write a LinkedIn post (1,300 characters max). Professional tone, include 3-5 relevant hashtags."

Step 4: Create short-form scripts
"Using these insights: [paste Step 1]
Write 3 scripts for 60-second videos (one per insight). Include hook, main point, and CTA."

Template 2: Email Campaign Builder

Use case: Create a complete email sequence

Step 1: Define campaign goals
"I'm launching [product] for [audience].
Create a campaign strategy with:
- Main goal
- Email sequence structure (how many emails, timing)
- Key message per email
- Primary CTA per email"

Step 2: Write email 1 (awareness)
"Using this strategy: [paste Step 1]
Write the first email. Introduce the problem [product] solves. Conversational tone, 150 words max."

Step 3: Write email 2 (consideration)
"Building on email 1: [paste Step 2]
Write email 2. Show how [product] works. Include one customer success story (make it realistic)."

Step 4: Write email 3 (conversion)
"Based on emails 1-2: [paste previous emails]
Write the final email. Present the offer, create urgency, make CTA obvious."

Template 3: Meeting-to-Action Chain

Use case: Turn meeting notes into actionable deliverables

Step 1: Structure notes
"Raw meeting notes: [paste notes]
Organize into:
- Key decisions made
- Action items (person, task, deadline)
- Open questions
- Follow-up needed"

Step 2: Create task breakdown
"From these action items: [paste action items from Step 1]
For each item, create:
- 3-5 subtasks
- Estimated time per subtask
- Dependencies (what needs to happen first)"

Step 3: Draft follow-up email
"Based on this meeting summary: [paste Step 1]
Write a follow-up email to attendees with:
- Brief recap of decisions
- Clear action items with owners
- Next meeting date/agenda"

Template 4: Product Research Sprint

Use case: Validate a product idea quickly

Step 1: Market scan
"Product idea: [describe product]
Target market: [describe market]

Find and summarize:
- 5 direct competitors
- 5 adjacent products (similar but different market)
- Common features across all
- Pricing ranges"

Step 2: User pain points
"Based on these competitors: [paste Step 1]
Research what users complain about in reviews. Identify top 5 recurring pain points."

Step 3: Feature gap analysis
"Given these pain points: [paste Step 2]
And these competitor features: [paste from Step 1]

What features are users asking for that NO competitor offers? List 5 opportunities."

Step 4: MVP scope
"From these opportunities: [paste Step 3]
Define an MVP (3-month build) that:
- Addresses top 2 pain points
- Includes 1 unique feature
- Is clearly different from competitors

List exact features and explain why each made the cut."

Template 5: Technical Documentation Chain

Use case: Document code or systems thoroughly

Step 1: High-level overview
"Codebase: [describe project/repo]
Write a README-style overview covering:
- What this project does (2-3 sentences)
- Key technologies used
- Main components/modules
- How components interact"

Step 2: Setup guide
"Based on this tech stack: [paste from Step 1]
Write step-by-step installation instructions:
- Prerequisites
- Installation commands
- Configuration needed
- How to verify it's working"

Step 3: Usage examples
"For these components: [paste components from Step 1]
Create 3 common usage examples with code snippets. Show actual implementation."

Step 4: Troubleshooting guide
"Given this system: [paste Step 1]
Create troubleshooting section with:
- 5 common errors users might hit
- Cause of each error
- Solution for each"

Common Mistakes and How to Avoid Them

Mistake 1: Losing Context Between Steps

What happens: Later prompts forget critical details from early steps.

Why it happens: You’re only pasting the output, not the full context.

Fix: Include relevant context from ALL previous steps in later prompts, not just the immediate previous one.

❌ Bad Step 3:
"Using this data: [paste Step 2 only]"

✅ Good Step 3:
"Original goal: [paste from Step 1]
Analysis so far: [paste Step 2]
Now create..."

Mistake 2: Making Steps Too Granular

What happens: You end up with 15 tiny steps that should’ve been 5.

Why it happens: Overthinking the breakdown.

Fix: Each step should produce a meaningful deliverable. If a “step” is just reformatting output, combine it with the previous step.

Rule of thumb: If you can’t imagine validating/editing a step’s output before moving on, it’s too small.

Mistake 3: Not Validating Between Steps

What happens: Step 1 produces garbage, but you keep running the chain. By Step 5, everything’s useless.

Why it happens: Treating chains like set-it-and-forget-it automation.

Fix: Actually read each output before proceeding. If Step 2 looks off, fix it or regenerate before Step 3.

Especially important when: Early steps are research/analysis (errors compound).

Mistake 4: Inconsistent Prompt Quality

What happens: Step 1 is detailed and specific, Step 3 is vague, output quality drops.

Why it happens: You get lazy or assume the AI “knows what you mean by now.”

Fix: Maintain the same level of specificity throughout. Don’t assume context carries over automatically.

❌ Bad Step 4:
"Now write the conclusion"

✅ Good Step 4:
"Write a conclusion (200 words) that:
- Summarizes the 3 main findings from [Step 2]
- Restates the recommendation from [Step 3]
- Ends with one clear next action"

Mistake 5: Over-Chaining Simple Tasks

What happens: You create a 6-step chain for something that needed one good prompt.

Why it happens: Chaining becomes a hammer, everything looks like a nail.

Fix: Ask yourself: “Would a single detailed prompt work here?” If yes, don’t chain just because you can.

Test: Try the single prompt first. If it works 80% of the time, you don’t need a chain.

Mistake 6: No Template Reuse

What happens: You manually rebuild the same chain every time you need it.

Why it happens: Not saving your working chains.

Fix: Document chains that work. Save them as templates with [bracketed placeholders] for variable parts.

Example template:

Step 1: Research [TOPIC]
Step 2: Analyze [specific aspect]
Step 3: Create [deliverable type]

Next time you need it, just fill in the brackets.

Wrapping Up: Your Prompt Chaining Action Plan

You now know how to build multi-step AI workflows that actually work. Here’s what to do next:

If you’re new to chaining:

  1. Start with the 2-step content chain (outline → article)
  2. Run it 3 times on different topics
  3. Notice how much better the output is vs. single prompts

If you’re ready to level up:

  1. Pick one of the 5 templates that matches your work
  2. Customize it for a real project
  3. Save the working version for reuse

If you’re already chaining:

  1. Audit your current chains for the 6 common mistakes
  2. Identify your most-run chain and automate it (LangChain or no-code tool)
  3. Calculate actual costs—you might be surprised

The big idea: Prompt chaining isn’t about making things complicated. It’s about breaking complexity into manageable steps. You wouldn’t build a house in one day, and you shouldn’t expect your AI to solve everything in one prompt.

Start simple, iterate, and save what works.

Got a prompt chain that works great? Share it in our skills directory—someone else is probably trying to solve the same problem.

Now go chain something.