Introduction: The "Build & Deploy" Mandate

Welcome to your Month 1 Execution Sprints. In the corporate tech world, engineers do not get paid to read textbooks; they get paid to ship working systems. Over the next 30 days, you will complete 10 highly specific technical workflows.

These workflows will bridge the gap between your university theory and modern Enterprise AI Architecture. You must complete these workflows sequentially. The outputs of these workflows will form the foundation of your Month 2 Capstone Projects.

Sprint 1: API & Data Automation (Workflows 1-5)

Goal: Master REST APIs, JSON structuring, and Serverless Webhooks.

Workflow 1: The REST API Ping

• Objective: Write your first production-level Python script to communicate with an LLM backend.
• Tools: Python 3.x, requests library, OpenAI/Gemini API Key.
• Execution Steps:
  1. Create a local .env file to securely store your API key. (Never hardcode keys into your main script).
  2. Write a Python script using the requests.post() method to send a simple prompt (e.g., "Explain REST APIs in one sentence") to the AI endpoint.
  3. Add basic error handling (if response.status_code == 200:).
• Completion Criteria: Your local terminal prints the AI's response successfully without throwing an authentication error.

Workflow 2: Strict JSON Parsing

• Objective: Force an AI model to output machine-readable data, not conversational text, and parse it in Python.
• Tools: Python, json library.
• Execution Steps:
  1. Modify your Workflow 1 script. Change the system prompt to: "You are a data API. Output only valid JSON. Do not include markdown formatting or conversational text."
  2. Ask the user prompt for a list of 3 tech companies, their CEOs, and their founding years.
  3. Use Python's json.loads() to parse the AI's response.
  4. Write a for loop to print only the CEO names to your terminal.
• Completion Criteria: The terminal prints a clean list of strings extracted directly from the AI's JSON payload.

Workflow 3: The Webhook Catcher

• Objective: Build a serverless listening node to catch live data over the internet.
• Tools: Make.com (Free Tier), Postman (or Python).
• Execution Steps:
  1. Create a new scenario in Make.com and add the "Custom Webhook" module.
  2. Generate the unique Webhook URL and copy it.
  3. Open Postman (or write a quick Python script) and send a POST request containing a dummy JSON payload (e.g., {"name": "John Doe", "issue": "Server down"}) to your Webhook URL.