Groq API: Build an AI Text Classifier That’s 10x Faster

Brenda and the Spreadsheet of Broken Dreams

Meet Brenda. Brenda works in operations. Her job, for eight hours a day, is to stare at a shared inbox overflowing with customer feedback. Her mission, which she has no choice but to accept, is to read each one and manually assign a category: ‘Bug Report’, ‘Feature Request’, ‘Billing Issue’, ‘Praise’.

She copies the email, pastes it into a spreadsheet, and selects a category from a dropdown. The praise gets sent to marketing. The bugs go to engineering. The billing issues go to finance. Brenda is the human router. Her brain is the most expensive, over-qualified, and saddest `if/then` statement in the entire company.

One day, the company runs a big promotion. The inbox explodes. Brenda is buried under a mountain of text. Important, time-sensitive bug reports get lost for hours. Hot sales leads go cold. Brenda starts to dream in spreadsheets.

This is a story about saving Brenda.

Why This Matters

We’re not just building a cool AI gadget. We’re building an instant, autonomous, and infinitely scalable version of Brenda. This workflow replaces the manual, soul-crushing task of sorting and routing information. For a business, this means:

• Speed: Customer issues are identified and routed to the right team in milliseconds, not hours.
• Scale: It can handle 10 emails a minute or 10,000. It doesn’t need a coffee break.
• Cost: We’re going to use a tool so mind-bogglingly cheap that it costs fractions of a penny to do what might take a human employee minutes.
• Sanity: It frees up smart people like Brenda to do work that requires a real brain—like talking to customers or solving complex problems—instead of acting as a biological copy-paste machine.

What This Tool / Workflow Actually Is

We’re using the Groq API to perform text classification.

Groq: Think of Groq as an F1 car. Other AI models run on GPUs, which are like powerful SUVs—they can do a lot of things pretty well. Groq runs on its own custom chip called an LPU (Language Processing Unit). The LPU is an F1 engine: it’s designed to do one thing—run AI models—with absolutely ludicrous speed. The result is performance that feels unreal, and because it’s so efficient, the cost is ridiculously low.

Text Classification: This is the simple act of putting a piece of text into a predefined box. We give the AI a message and a list of possible categories, and it tells us which box the message belongs in. That’s it. It’s a fundamental building block of countless business automations.

What it is NOT: This is not a super-intelligent general AI that understands nuance like a human. It is a highly-specialized, lightning-fast sorting hat for text. It’s brilliant at its one job, and we’re going to exploit that.

Prerequisites

I know the word “API” and “script” can be scary. Relax. If you can follow a recipe to bake a cake, you can do this. Here’s what you need:

1. A Groq API Key: Go to the GroqCloud Console. Sign up for a free account and create an API key. Copy it somewhere safe. It’s free to get started and the rates are tiny.
2. Python 3 installed: Most computers have it already. If not, a quick Google search for “install python” will get you there. We only need it to run a simple script. No coding experience required.
3. An idea: What do you want to classify? Think of your categories beforehand. For example: `Sales Lead`, `Support Ticket`, `Spam`.

That’s it. No credit card, no complex server setup. Just you, me, and a very fast robot intern.

Step-by-Step Tutorial

Let’s build our classifier. We’re going to write a simple Python script that takes a piece of text and asks Groq to categorize it.

Step 1: Install the Groq Python Library

Open your terminal or command prompt. This is the little black window where you can type commands to your computer. Type this and press Enter:

pip install groq

This is like installing an app for Python. It gives our script the tools it needs to talk to Groq.

Step 2: Create Your Python Script

Create a new file and name it `classifier.py`. You can use any simple text editor (like Notepad on Windows or TextEdit on Mac). Paste the following code into the file.

Step 3: The Code – Your Digital Sorting Machine

This is the entire machine. I’ll explain what each part does right below it. Copy this exactly.

import os
from groq import Groq

# --- CONFIGURATION ---
# Paste your Groq API key here. NEVER share this key.
# It's better to set this as an environment variable for security.
client = Groq(
    api_key="YOUR_GROQ_API_KEY_HERE",
)

# --- THE BRAIN OF OUR CLASSIFIER ---
# This is the instruction manual for our AI. Be clear and specific.
system_prompt = """
You are an expert text classification AI. Your job is to analyze the user's text and classify it into ONE of the following categories:
[Sales Inquiry, Technical Support, Billing Question, General Feedback]

You MUST respond in JSON format with two keys:
1. 'category': The single best category from the list.
2. 'urgency': A score from 1 (low) to 5 (high) representing the urgency.

Do not add any explanations or introductory text. Only return the JSON object.
"""

# --- THE INPUT ---
# This is the text we want to classify. We'll make this dynamic later.
text_to_classify = "Hi there, I was wondering about your enterprise pricing. Also, my login seems to be broken."


# --- THE MAGIC HAPPENS HERE ---
def classify_text(text):
    print("Sending text to Groq for classification...")
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": system_prompt,
            },
            {
                "role": "user",
                "content": text,
            }
        ],
        # We use Llama 3 8b because it's fast, cheap, and great at this.
        model="llama3-8b-8192",
        
        # These two settings are CRITICAL for reliable classification
        temperature=0,  # 0 = No creativity, just follow instructions
        response_format={"type": "json_object"}, # Force JSON output
    )
    
    response_content = chat_completion.choices[0].message.content
    print("--- Groq's Response ---")
    print(response_content)
    print("----------------------")
    return response_content

# --- RUN THE CLASSIFIER ---
classify_text(text_to_classify)

Step 4: Understand and Customize the Code

• api_key="YOUR_GROQ_API_KEY_HERE": Replace this placeholder with the actual key you copied from your Groq dashboard.
• system_prompt: This is the most important part. This is where you give the AI its job description. Notice how clear it is. I’ve defined the exact categories (`[Sales Inquiry, Technical Support, …]`) and told it to respond *only* in JSON format. You should change these categories to match your own business needs.
• text_to_classify: This is the sample input. Change this to test different messages.
• model="llama3-8b-8192": We’re using a small, fast model. It’s more than enough for this task.
• temperature=0: This tells the AI: “Don’t be creative. Be a robot. Be predictable.” This is crucial for classification.
• response_format={"type": "json_object"}: This is a magic bullet. We are forcing the model to give us clean, structured JSON. This makes the output reliable and easy for other computer systems to understand.

Step 5: Run It!

Save the file. Go back to your terminal, navigate to the folder where you saved `classifier.py`, and run this command:

python classifier.py

You should see a result almost instantly. It will look something like this:

Sending text to Groq for classification...
--- Groq's Response ---
{
  "category": "Sales Inquiry",
  "urgency": 4
}
----------------------

Look at that. It correctly identified that the message was a mix of things, but prioritized the sales part. It gave us clean, structured data that another machine can now use. You just built an AI classifier in less than 10 minutes.

Complete Automation Example

Let’s save Brenda. Her company uses a simple contact form that sends emails to `feedback@brendas-company.com`.

The Goal: Instead of Brenda reading every email, an automation will read it, classify it using our Groq script, and then forward it to the correct department’s Slack channel.

The Workflow:**
1. **Trigger:** A new email arrives in the feedback inbox. (We’d use a tool like Zapier, Make.com, or a simple script with the Gmail API for this).
2. **Action:** The email body is extracted and sent as the `text_to_classify` to our Python script.
3. **Classification:** Our Groq script runs and returns the JSON, for example: `{“category”: “Technical Support”, “urgency”: 5}`.
4. **Routing (The ‘if/then’ part):** Another part of our automation reads the JSON.
* If `category` is `Technical Support`, post a message to the `#engineering-alerts` Slack channel.
* If `category` is `Billing Question`, create a ticket in Stripe for the finance team.
* If `category` is `Sales Inquiry`, create a new lead in the CRM and assign it to a sales rep.

Brenda is now free. She no longer lives in the inbox. She’s talking to high-value customers and improving processes. She got a promotion. We saved Brenda.

Real Business Use Cases (MINIMUM 5)

This exact same pattern can be used everywhere:

1. E-commerce Store: Ingest all new product reviews. Classify them as `Shipping Feedback`, `Product Quality Issue`, `Positive Review`, or `Sizing Problem`. A dashboard can then track trends in real time without anyone reading thousands of reviews.
2. Marketing Agency: Monitor Twitter mentions of a client’s brand. Classify each tweet’s sentiment as `Positive`, `Negative`, or `Neutral`. Automatically alert the PR team via Slack if a high-profile account posts something `Negative`.
3. SaaS Company: Analyze incoming support tickets from Zendesk or Intercom. Classify them by feature area (`Dashboard`, `API`, `Integrations`) and type (`Bug`, `How-to Question`, `Outage`). This instantly routes the ticket to the specialist team.
4. Real Estate Agency: Process inbound website contact forms. Classify leads as `Buyer`, `Seller`, `Renter`, or `Tire-kicker`. Hot `Buyer` leads can trigger an instant SMS to an agent on duty.
5. Content Creator: Go through all YouTube comments on a new video. Classify them as `Question`, `Video Idea`, `Praise`, or `Spam`. Automatically add the `Video Idea` comments to a Trello board for future content planning.

Common Mistakes & Gotchas

• Vague Categories: Don’t use categories that overlap, like “Urgent” and “Important.” The AI will get confused. Make them distinct and clear. `[Bug Report]` is better than `[Problem]`.
• Chatty System Prompts: Be direct. Don’t say “Could you please try to…”. Say “You are an AI. Your job is to… You MUST respond in JSON.” Be firm with your robot intern.
• Forgetting `temperature=0`: For classification, you want deterministic, repeatable results. Any temperature above 0 introduces randomness, which is the enemy of a reliable sorting system.
• Not Forcing JSON Mode: The `response_format={“type”: “json_object”}` is a gift. Without it, the model might sometimes decide to add helpful text like “Sure, here is the JSON you requested:”. This breaks automations that expect *only* JSON.
• Not Planning for Failure: What if the Groq API is down (unlikely, but possible)? Or the internet connection blips? A production-ready script would wrap the API call in a `try/except` block to catch errors gracefully instead of crashing.

How This Fits Into a Bigger Automation System

Our little classifier script is a single, powerful gear in a much larger machine. It’s the central nervous system for routing information.

Think of it as a function: `classify(input_text) -> structured_data`.

• The Input can come from anywhere: a webhook from your CRM, a new row in a Google Sheet, a message from a voice agent that transcribed a customer voicemail, a user query in a RAG system.
• The Output (our clean JSON) can trigger anything: it can update a Salesforce record, send a formatted email via SendGrid, create a task in Jira, or even be the input for *another* AI agent that decides what to do next.

This isn’t a standalone tool; it’s the decision-making engine you plug into the pipes of your business.

What to Learn Next

Fantastic. You now have a script that can classify text at superhuman speed. It’s like having an F1 car, but right now it’s just sitting in the garage. Running it manually from your command line is cool, but it’s not *automation*.

The next logical step is to get it out of the garage and onto the racetrack. We need to hook it up to the real world so it can run automatically, without you even thinking about it.

In our next lesson in the AI Automation Academy, we’re going to do just that. We’ll learn “How to Deploy a Python Script as a Serverless API.” We’ll take this exact script and turn it into a live endpoint that any other service (like Zapier, your website, or your CRM) can talk to. That’s when the real magic begins.

Stay tuned. You’ve just built the brain; next, we build the body.

“,
“seo_tags”: “groq api, text classification, ai automation, python, large language models, business automation, workflow automation, cheap ai api”,
“suggested_category”: “AI Automation Courses