Multi-Step AI Workflows

Simple automations use one AI step. But real business problems often require multiple AI steps working together, each building on the previous one's output.

When Single AI Steps Aren't Enough

Scenario	Why Multiple Steps?
Content localization	Translate → Adapt cultural references → Format for platform
Lead processing	Extract info → Score lead → Generate response → Determine routing
Document analysis	Summarize → Extract entities → Classify → Generate action items
Customer feedback	Detect language → Translate → Analyze sentiment → Categorize → Suggest response

Chaining AI Steps: The Pipeline Pattern

WORKFLOW: Customer Feedback Processor
──────────────────────────────────────
TRIGGER: New survey response received
    ↓
AI STEP 1: Language Detection
  Input: Raw feedback text
  Output: Language code (en, ar, es, fr...)
    ↓
CONDITIONAL: If language ≠ English
    ↓
AI STEP 2: Translation
  Input: Original text + source language
  Output: English translation
    ↓
AI STEP 3: Sentiment Analysis
  Input: English text (original or translated)
  Output: sentiment (positive/neutral/negative), score (1-10)
    ↓
AI STEP 4: Category Classification
  Input: Feedback text
  Output: category (product/service/pricing/support)
    ↓
AI STEP 5: Response Generation
  Input: Original feedback + sentiment + category
  Output: Suggested response in original language
    ↓
ACTION: Create ticket with all enriched data

Key Chaining Techniques

1. Variable Passing

Each AI step's output becomes available for subsequent steps:

Step 1 Output → {{step1.sentiment}}
Step 2 can use → "Based on the {{step1.sentiment}} sentiment..."
Step 3 can use → Both {{step1.sentiment}} AND {{step2.category}}

2. Conditional Branching

Route workflow based on AI decisions:

WORKFLOW: Smart Email Router
────────────────────────────
TRIGGER: New email received
    ↓
AI STEP: Analyze and Classify
  Outputs:
    - type: support/sales/billing/spam
    - urgency: high/medium/low
    - language: detected language
    ↓
PATH A: If type = "spam"
  → Move to spam folder
  → END
    ↓
PATH B: If type = "support" AND urgency = "high"
  → Create urgent ticket
  → Send Slack alert to on-call
  → Auto-acknowledge to customer
    ↓
PATH C: If type = "sales"
  → Add to CRM
  → Assign to sales rep based on language
  → Schedule follow-up task
    ↓
PATH D: Default
  → Create standard ticket
  → Route to appropriate queue

3. Aggregation Pattern

Collect multiple AI analyses, then combine:

WORKFLOW: Comprehensive Content Brief
─────────────────────────────────────
TRIGGER: New content request form submitted
    ↓
┌─────────────────────────────────────────────┐
│  PARALLEL AI STEPS (run simultaneously)     │
├─────────────────────────────────────────────┤
│ AI STEP A: Keyword Research                 │
│   → Top 10 target keywords                  │
│                                             │
│ AI STEP B: Competitor Analysis              │
│   → Summary of top 3 competitor articles    │
│                                             │
│ AI STEP C: Audience Insights                │
│   → Pain points and questions               │
└─────────────────────────────────────────────┘
    ↓
AI STEP: Aggregate & Create Brief
  Input: Results from A + B + C
  Output: Comprehensive content brief
    ↓
ACTION: Create document in Google Docs

Prompt Design for Multi-Step Workflows

Keep Each Step Focused

❌ BAD: One mega-prompt
"Analyze this email. Detect the language, translate if needed,
determine sentiment, classify the topic, extract any mentioned
products, identify the customer's intent, and write a response."

✅ GOOD: Focused steps
Step 1: "What language is this text? Reply with only the
        ISO language code (e.g., en, ar, es)."

Step 2: "Translate this {{step1.language}} text to English.
        Preserve tone and meaning."

Step 3: "Rate the sentiment of this text from 1-10 where
        1 is very negative and 10 is very positive.
        Reply with only the number."

Use Structured Outputs

Request consistent formats for easier automation:

PROMPT: "Analyze this customer feedback and respond in JSON:
{
  'sentiment': 'positive' or 'negative' or 'neutral',
  'category': one of ['product', 'service', 'pricing', 'other'],
  'urgency': 'high' or 'medium' or 'low',
  'key_issues': [list of main points],
  'suggested_action': brief recommendation
}"

Blueprint: Multi-Language Support Ticket Handler

WORKFLOW: Global Support Ticket Processor
─────────────────────────────────────────
TRIGGER: New support email received
    ↓
AI STEP 1: Initial Analysis
  Prompt: "Analyze this support email and provide:
          1. Language (ISO code)
          2. Urgency (high/medium/low)
          3. Technical complexity (simple/moderate/complex)"
    ↓
CONDITIONAL: If language ≠ "en"
  │
  ├── AI STEP 2a: Translate to English
  │     Input: Original email
  │     Output: English translation for agent
  │
  └── Store original language for response
    ↓
AI STEP 3: Deep Classification
  Prompt: "Classify this support request:
          - Product area: [list your products]
          - Issue type: bug/how-to/feature-request/billing
          - Required expertise: L1/L2/L3"
    ↓
AI STEP 4: Generate Response Draft
  Prompt: "Write a helpful response to this support request.
          Be empathetic and solution-focused.
          Include: acknowledgment, initial guidance, next steps."
    ↓
CONDITIONAL: If original language ≠ "en"
    ↓
AI STEP 5: Translate Response
  Input: English response draft
  Output: Response in customer's language
    ↓
ACTION: Create ticket with:
  - Original message
  - English translation (if applicable)
  - Classification data
  - Response draft (in customer's language)
  - Priority based on urgency + complexity

Performance Considerations

Factor	Recommendation
Speed	Parallelize independent AI steps when possible
Cost	Use smaller models for simple tasks (translation, classification)
Reliability	Add fallback logic if any AI step fails
Debugging	Log each step's input/output for troubleshooting

Cost-Efficient Model Selection

Step Type         → Recommended Model
────────────────────────────────────────
Language detection → GPT-4o mini / Gemini Flash (cheap, fast)
Translation        → GPT-4o mini (good quality, low cost)
Sentiment analysis → GPT-4o mini (simple task)
Complex reasoning  → GPT-4o / Claude Sonnet (when quality matters)
Creative writing   → GPT-4o / Claude Sonnet (nuance needed)

Common Pitfalls

Pitfall	Solution
Context loss	Pass relevant context from earlier steps explicitly
Error cascade	If Step 2 fails, don't run Steps 3-5
Inconsistent formats	Define output structure in every prompt
Over-chaining	Not every task needs multiple AI steps—keep it simple

Key Insight: Multi-step workflows are powerful but add complexity. Start with the simplest approach that works, then add steps only when single-step solutions prove insufficient.

Next: Learn about AI Agents—when to let AI make decisions autonomously. :::

Quiz