Blog Details

Scenario:

You want to connect multiple systems—for example, a database, a web API, and a CSV report—to automate a workflow. Writing all the integration code manually is tedious. Your AI assistant can generate scripts that integrate these systems efficiently.

Step 0: Define Your Goal

Example workflow:

1. Fetch customer data from a database
2. Call an external API to enrich customer information (e.g., validate email)
3. Save the results into a CSV report

Goal: Generate a Python script that performs all steps automatically.

Step 1: Craft the AI Prompt

Treat AI as a workflow automation developer. Include:

1. Systems involved (database, API, CSV)
2. Operations to perform (fetch, validate, save)
3. Optional: error handling, logging, and libraries to use

Example Prompt:

Write a Python script that:
1. Reads customer data from a PostgreSQL database (columns: id, name, email)  
2. Validates emails using an external API  
3. Writes the validated results into a CSV file  
Include error handling, logging, and comments explaining each step.

Step 2: AI Output Example

import psycopg2
import requests
import csv

# Connect to PostgreSQL
conn = psycopg2.connect(dbname="mydb", user="user", password="pass", host="localhost")
cur = conn.cursor()
cur.execute("SELECT id, name, email FROM customers")
customers = cur.fetchall()

validated_customers = []

for customer in customers:
    id, name, email = customer
    try:
        response = requests.get(f"https://api.emailverify.com/verify?email={email}")
        response.raise_for_status()
        is_valid = response.json()['valid']
        validated_customers.append((id, name, email, is_valid))
    except requests.RequestException as e:
        print(f"Error validating {email}: {e}")

# Write results to CSV
with open("validated_customers.csv", "w", newline="") as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(["ID", "Name", "Email", "Valid"])
    writer.writerows(validated_customers)

cur.close()
conn.close()
print("Integration script completed successfully.")

Step 3: Test the Script

• Ensure your database credentials and API key are correct
• Run the script and verify the CSV output
• Check error handling and logging for robustness

Step 4: Mini Lab Challenges

1. Modify the script to handle multiple APIs and merge results.
2. Automate daily execution using cron or Task Scheduler.
3. Include data transformations before writing to CSV (e.g., formatting names).
4. Challenge: Integrate Excel reports instead of CSV using openpyxl.

Step 5: Pro Tips

• Clearly specify systems, APIs, and output format in the prompt
• Ask AI to include error handling, logging, and comments
• Test incrementally: database fetch → API call → CSV output
• Use AI to adapt scripts to different environments or endpoints
• Iteratively refine prompts for efficiency and readability

Lab Summary

• AI can generate integration scripts connecting databases, APIs, and files
• Clear prompts + workflow description = automated, tested scripts
• AI helps reduce manual coding and integration errors
• Using AI for integration increases productivity and ensures repeatable workflows