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Chapter 2: Building AI-Powered Workflows

Chaining Prompts​

Complex tasks often require multiple AI calls:

WORKFLOW: Content Creation Pipeline

Step 1: Research
Prompt: "Research \{TOPIC\}. Provide key facts, statistics, 
        and recent developments."
Output β†’ Input for Step 2

Step 2: Outline
Prompt: "Using this research: \{STEP1_OUTPUT\}
        Create a blog post outline with 5 sections."
Output β†’ Input for Step 3

Step 3: Draft
Prompt: "Following this outline: \{STEP2_OUTPUT\}
        Write a complete 800-word blog post."
Output β†’ Input for Step 4

Step 4: Edit
Prompt: "Edit this draft for: \{STEP3_OUTPUT\}
        - Grammar and clarity
        - SEO optimization
        - Consistent tone"
Output β†’ Final content

Automation Pipelines​

Document Processing Pipeline​

INPUT: Raw document

β†’ Step 1: Extract (key information)
β†’ Step 2: Classify (document type)
β†’ Step 3: Summarize (executive summary)
β†’ Step 4: Route (to appropriate team)
β†’ Step 5: Action (generate response if needed)

OUTPUT: Processed document + actions

Email Triage System​

For each incoming email:

1. CATEGORIZE
   β†’ Support / Sales / General / Spam / Urgent

2. EXTRACT
   β†’ Sender intent, key requests, deadline

3. DRAFT RESPONSE
   β†’ Based on category and templates

4. ROUTE
   β†’ To appropriate person/queue

5. LOG
   β†’ Track metrics for optimization

Integration Patterns​

API Workflow​

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   Input     │───▢│  AI Model   │───▢│   Output    β”‚
β”‚   Source    β”‚    β”‚  (Prompt)   β”‚    β”‚   Action    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
     β”‚                   β”‚                   β”‚
  Database          OpenAI/Claude        Email/Slack
  Webhook           Anthropic            Database
  Form             Custom Model          Webhook

Error Handling​

Workflow with error handling:

try:
  result = call_ai_with_prompt(prompt, input)
  if quality_check(result):
    return result
  else:
    return retry_with_refined_prompt()
catch:
  return fallback_response()
  alert_human_for_review()

Best Practices​

  1. Start simple - Get one step working before chaining
  2. Validate between steps - Check output quality
  3. Include fallbacks - Human review for failures
  4. Log everything - For debugging and optimization
  5. Monitor costs - Track token usage

Key Takeaways​

Summary
  1. Chain prompts for complex tasks
  2. Build automation pipelines step by step
  3. Integrate with existing tools via APIs
  4. Include error handling and fallbacks
  5. Monitor and optimize continuously

← Previous: Business Use Cases | Next: Future of Prompt Engineering β†’