AI Data Extraction with Groq: The 10-Minute Guide

Welcome back to the Academy. Class is in session.

I once had a client, a frantic e-commerce founder, who was drowning in customer support emails. To cope, she hired a team of three interns. Their entire job, eight hours a day, was to read emails, identify the customer’s name, order number, and issue, and then copy-paste that information into a spreadsheet. The spreadsheet was a chaotic battlefield of typos, missed details, and desperation.

The interns were slow. They were inconsistent. And let’s be honest, they were probably questioning their life choices. The founder was paying for human brains to do the work of a dumb machine. This is the definition of insanity in the age of AI.

Today, we’re going to build the robot that replaces that entire soul-crushing process. And we’re going to do it in about 10 minutes, using an engine so fast it feels like magic.

Why This Matters

Manual data entry is a tax on your business. It’s slow, expensive, and scales horribly. Every minute a human spends copying and pasting is a minute they’re not spending on strategy, sales, or customer relationships.

This isn’t just about saving an intern’s salary. It’s about building an information pipeline that works at the speed of light. Imagine every lead form, every support ticket, and every customer review being instantly and perfectly categorized, structured, and ready for action. No delays. No human error. No overflowing inboxes.

We’re replacing a bucket brigade with a high-pressure firehose. The data gets where it needs to go, instantly. This unlocks real-time dashboards, immediate support ticket routing, and lead qualification that happens before the prospect has even closed their browser tab. That’s not just efficiency; it’s a competitive advantage.

What This Tool / Workflow Actually Is

We’ll be using a tool called Groq (pronounced “grok,” like the word for understanding deeply).

What it is: Groq is an AI inference company that has built specialized computer chips (LPUs, or Language Processing Units) designed to run Large Language Models (LLMs) at absolutely absurd speeds. Think hundreds of tokens per second. It’s so fast it feels fake the first time you see it.

What we’re doing with it: We are using Groq’s speed for Structured Data Extraction. This is a fancy term for a simple concept: teaching an AI to read messy, unstructured text (like an email) and force it to output clean, structured data (like JSON) that a computer can easily understand. We give the AI a strict template, and its only job is to fill it out. No chit-chat, no creative writing—just the facts.

What it is NOT: This is not a tool for writing your next novel or having a deep philosophical conversation. We’re using it as a specialized, high-speed parsing engine. It’s a precision tool, not a swiss army knife.

Prerequisites

This is where people get nervous. Don’t be. If you can follow a recipe to bake a cake, you can do this. I’m being brutally honest here:

A Groq Account: Go to console.groq.com and sign up. They have a generous free tier. Once you’re in, go to the “API Keys” section and create a new key. Copy it and save it somewhere safe. This is your password to the magic kingdom.
Python 3 installed on your computer: If you don’t have it, a quick Google search for “install Python on [your operating system]” will get you there in five minutes. You do NOT need to be a Python expert. You just need it to be present.

That’s it. No credit card, no complex server setup. Just you, a text editor, and a little bit of copy-pasting.

Step-by-Step Tutorial

Alright, let’s build our data-extracting robot. We’re going to write a simple Python script. Don’t panic. I’ll explain every single line.

Step 1: Set Up Your Project

Open a terminal or command prompt. Create a new folder for our project and navigate into it. Now, we need to install two small libraries.

pip install groq pydantic

groq is the official library to talk to the Groq API. pydantic is a brilliant tool that helps us define the “form” or “template” we want the AI to fill out. It enforces the rules so the AI can’t mess up.

Step 2: Create Your Python File

Create a file named extract.py and open it in your favorite text editor.

Step 3: Define Your Data Structure

This is the most important part. We need to tell the AI *exactly* what information we want. We’ll use Pydantic to create a class. Think of a class as a blueprint. We’re creating the blueprint for our perfect, structured data.

Add this code to your extract.py file:

# Import the necessary libraries
import os
from groq import Groq
from pydantic import BaseModel, Field

# This is our data template
# We define what we want to extract from the text
class SupportTicket(BaseModel):
    customer_name: str = Field(description="The full name of the customer.")
    customer_email: str = Field(description="The email address of the customer.")
    order_id: str = Field(description="The order number mentioned in the text.")
    summary: str = Field(description="A brief, one-sentence summary of the customer's issue.")
    urgency: str = Field(description="The urgency of the issue, categorized as 'Low', 'Medium', or 'High'.")

See how clear that is? We’ve created a `SupportTicket` template with five fields. We even gave each field a `description` to help the AI understand exactly what to look for. This clarity is what makes the system reliable.

Step 4: Write the Code to Call Groq

Now, let’s add the code that sends our messy text to Groq and tells it to use our template. Add the following below the code from Step 3.

# --- Main execution part of the script ---

# 1. Set up the Groq client
# Make sure to set your GROQ_API_KEY as an environment variable
# or replace os.environ.get("GROQ_API_KEY") with your actual key as a string.
client = Groq(
    api_key=os.environ.get("GROQ_API_KEY"),
)

# 2. Define the messy text we want to process
messy_email_text = """
Hi there,
My name is Brenda Smith and I'm having a problem with my recent order, #G-12345. 
It hasn't arrived yet and I'm getting worried. My email is brenda@example.com. 
Please help, this is pretty urgent!!
Thanks,
Brenda
"""

# 3. Make the API call to Groq
chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "system",
            "content": "You are an expert at extracting information from user-provided text and formatting it as a JSON object based on the provided schema."
        },
        {
            "role": "user",
            "content": messy_email_text,
        }
    ],
    model="llama3-70b-8192",
    # This is the magic part!
    # We tell the model to use our SupportTicket tool and we REQUIRE it.
    tool_choice="required", 
    tools=[
        {
            "type": "function",
            "function": {
                "name": "create_support_ticket",
                "description": "Create a support ticket from the user's text.",
                "parameters": SupportTicket.model_json_schema()
            },
        }
    ],
)

# 4. Print the clean, structured output
print(chat_completion.choices[0].message.tool_calls[0].function.arguments)

Let’s break down the magic: The `tool_choice=”required”` part is critical. It’s us telling the AI, “Do not talk back to me. Do not say ‘Sure, here is the information.’ Your ONLY valid response is to fill out the form I gave you.” This simple command turns a creative chatbot into a reliable data processing engine.

Before you run it, make sure to set your API key. The best way is an environment variable, but for a quick test, you can just paste your key in directly: `api_key=”YOUR_GROQ_API_KEY_HERE”`.

Complete Automation Example

Let’s run our creation. Open your terminal in the same folder as your `extract.py` file and run the script:

python extract.py

In less than a second, you will see this beautiful, clean output:

{
  "customer_name": "Brenda Smith",
  "customer_email": "brenda@example.com",
  "order_id": "G-12345",
  "summary": "The customer's order has not arrived and she is worried.",
  "urgency": "High"
}

Look at that. From a messy, multi-line email to perfect, structured JSON. The AI correctly identified every piece of information, summarized the issue, and even inferred the urgency based on the tone. That team of three interns? Their entire week’s work can now be done in minutes, with zero errors.

Real Business Use Cases

This exact same pattern can be used across hundreds of business functions. You just change the Pydantic model (the template) to fit your needs.

Marketing Agency: Parse inbound lead emails or contact form submissions. Extract `name`, `company_size`, `budget`, `service_needed`, and `timeline` to automatically create a new deal in your CRM and assign it to the right salesperson.
Legal Tech: Analyze a legal document to extract `contract_date`, `party_names`, `renewal_date`, and `liability_clause_text`. This data can populate a contract management system, sending reminders before a renewal.
Real Estate: Process property listings to pull out `address`, `square_footage`, `number_of_bedrooms`, `price`, and `agent_contact_info`. Use this to feed a database for market analysis.
Recruiting Firm: Scan resumes (as plain text) to extract `candidate_name`, `years_of_experience`, `key_skills` (as a list), and `previous_employers`. This can pre-screen candidates and match them to open roles automatically.
Financial Services: Process news articles or press releases to extract `company_name`, `stock_ticker`, `event_type` (e.g., ‘earnings release’, ‘merger’), and `sentiment` (‘positive’, ‘negative’, ‘neutral’).

Common Mistakes & Gotchas

Forgetting tool_choice="required": I’m saying it again because it’s that important. If you omit this, the model might just respond conversationally, breaking your automation. You must force it to use your tool.
Vague Field Descriptions: The AI is smart, but it’s not a mind reader. If your Pydantic model has a field called `data` with no description, you’ll get garbage. Be specific. `customer_shipping_address` is better than `address`.
Not Handling Missing Information: What if an email doesn’t mention an order number? You can make fields optional in Pydantic (e.g., `order_id: Optional[str] = Field(…)`). Your code needs to be prepared for `null` values.
Thinking Speed Solves Everything: Groq is incredibly fast, but the model’s accuracy still depends on the prompt and the input text. If the text is gibberish, the output will be too. Garbage in, garbage out—just really, really fast garbage.

How This Fits Into a Bigger Automation System

This script is not an island. It’s a powerful component—the “Intake Valve” for a much larger factory.

Think about it. The clean JSON this script produces is now the universal language for other systems. You can now easily connect this to:

A CRM: Take the JSON and make an API call to HubSpot, Salesforce, or Airtable to create or update a contact and a deal.
An Email System: Based on the `urgency` field, you could trigger an automated, high-priority email reply via SendGrid or Mailgun.
A Project Management Tool: Automatically create a new ticket in Jira or a new card in Trello, assigning it to the right support agent.
A Voice Agent: The input text doesn’t have to be an email. It could be the transcript from a customer’s phone call. This script can parse the conversation and turn it into a structured record.
Multi-Agent Workflows: This script can be Agent #1. Its job is to extract the `order_id`. It then hands that clean ID to Agent #2, whose job is to look up the order status in a Shopify database. This is how you build complex, autonomous systems.

What to Learn Next

You’ve just built a machine that can read and understand text with superhuman speed and accuracy. You’ve turned chaos into structure. Take a moment to appreciate that. This is a foundational skill for any serious builder in AI automation.

But right now, our structured data is just printing to the screen. It’s trapped.

In the next lesson in this course, we’re going to set it free. We will take this exact script and connect it to the outside world. We’ll learn how to wrap it in a simple API and use webhooks to automatically trigger this workflow every time a new email arrives or a form is submitted. We’re going to build a true, end-to-end pipeline: from a messy customer email to a perfectly organized Trello card, all happening in the blink of an eye, with zero human intervention.

You’ve built the engine. Next, we build the rest of the car.

Stay sharp. I’ll see you in the next class.

— Professor Ajay

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