Tips & Tricks: Use LLMs for Text Classification Tasks using ChatGPT

Tips & Tricks: Use LLMs for Text Classification Tasks using ChatGPT. Get practical lessons and hands-on examples at AIComputerClasses in Indore to master artificial intelligence (AI) skills quickly. Follow practical exercises and tool-based examples to learn rapidly. Ideal for beginners and working professionals seeking fast skill gains. Includes references to tools like ChatGPT, Power BI, Excel, Figma, or Python where appropriate.

 Tips & Tricks: Use LLMs for Text Classification Tasks using ChatGPT

In 2025, Large Language Models (LLMs) like ChatGPT have revolutionized how we handle text classification — from filtering spam to analyzing customer sentiment and automating document tagging. What once required extensive manual labeling or complex model training can now be achieved in minutes with simple, structured prompts.

At AI Computer Classes – Indore, students learn how to apply LLMs like ChatGPT to real-world NLP (Natural Language Processing) tasks with hands-on examples. Whether you’re a beginner or a professional aiming to sharpen your AI skills, this guide will help you understand how to efficiently perform text classification using LLMs.

💡 What is Text Classification?

Text classification is the process of categorizing text into predefined labels. For example:

• Classifying reviews as positive or negative
• Sorting emails into work, personal, or spam
• Categorizing news articles by topic (politics, sports, finance)

Traditionally, this required complex machine learning pipelines involving tokenization, feature extraction, and training models like Naïve Bayes or BERT. But with LLMs like ChatGPT, you can skip those steps and perform classification directly using prompt engineering.

⚙️ Why Use LLMs for Text Classification?

LLMs understand language contextually, not just statistically. This allows them to classify nuanced text — even when labels overlap or the language is informal.

✨ Key Advantages:

• Zero-shot learning: No need to train on labeled data.
• Few-shot examples: Provide just a few examples in your prompt.
• Context awareness: Handles slang, sarcasm, and sentiment variations.
• Multi-label support: Can assign multiple tags per input.

At AI Computer Classes – Indore, learners experiment with both zero-shot and few-shot text classification using ChatGPT’s API and Python notebooks.

🧠 Step-by-Step: Perform Text Classification using ChatGPTStep 1: Define Your Categories

Before asking ChatGPT to classify, clearly specify your target classes.

Example categories:

• Sentiment → Positive, Negative, Neutral
• Intent → Purchase, Query, Complaint
• Domain → Finance, Tech, Health, Education

Step 2: Create a Clear Prompt

Here’s an example of a zero-shot prompt:

Classify the following customer review into Positive, Negative, or Neutral:
"Great service, fast delivery, but packaging could be better."

💬 ChatGPT will respond:

Sentiment: Positive

You can also include a few-shot example for better accuracy:

Classify the following sentences as Positive, Negative, or Neutral:
1. "Loved the product!" → Positive  
2. "The delivery was late and frustrating." → Negative  
3. "It’s okay, nothing special." → Neutral  
Now classify: "Great service, fast delivery, but packaging could be better."

Step 3: Use ChatGPT with Python

You can automate classification using the OpenAI API.

Here’s a simple Python snippet:

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Classify this text: 'The food was amazing but the service was slow.' into Positive, Negative, or Neutral."}]
)

print(response.choices[0].message.content)

💡 This approach is perfect for processing hundreds of reviews or messages automatically.

Step 4: Structure Results in Excel or Power BI

Once you collect classifications, you can visualize them:

• Excel: Create pie charts showing positive vs. negative distribution.
• Power BI: Build dashboards that analyze customer feedback trends over time.

📊 Students at AI Computer Classes – Indore learn to integrate AI output with Excel and Power BI for end-to-end automation.

Step 5: Handle Multi-Label Classification

Some texts belong to more than one category.

Example:

“The new software update improved speed but caused bugs.”

Prompt:

Identify applicable categories for this text:
["Performance", "Bugs", "User Experience", "Design"]

ChatGPT may respond:

["Performance", "Bugs"]

Step 6: Optimize Accuracy with Prompt Tuning

Tips for better classification:

• Be explicit about available categories.
• Use few-shot examples for complex tasks.
• Add context clues like language or domain.
• Validate model outputs manually for critical applications.

🚀 At AI Computer Classes – Indore, we teach prompt optimization techniques so learners can achieve professional-grade accuracy using AI tools.

🔧 Bonus: Build a Simple Text Classifier App

You can design a text classification app using Figma for UI and Python backend.

Here’s the workflow:

1. Design an input box and category display in Figma.
2. Connect the input field to a Python API endpoint.
3. Use ChatGPT to process classification.
4. Display the output in real-time.

💡 This mini-project helps students connect design, logic, and AI seamlessly.

🧩 Real-World Applications

Use CaseDescriptionCustomer Feedback AnalysisClassify reviews for sentiment and product insightsEmail FilteringAutomatically detect spam or categorize messagesNews TaggingLabel news articles into relevant domainsSocial Media MonitoringTrack trending topics and brand mentionsSupport Ticket RoutingAssign tickets to correct departments automatically

Each of these can be implemented using ChatGPT-powered classification, saving time and increasing efficiency.

💡 Learn from Experts at AI Computer Classes – Indore!

Boost your career with hands-on courses in AI, Data Science, and Computer Applications.

Learn to apply LLMs, Python, and prompt engineering to solve real-world business and technical challenges.

👉 Join our latest batch now at AI Computer Classes

📍 Located in Old Palasia, Indore

🏁 Conclusion

Text classification is a cornerstone of modern AI systems — from search engines to recommendation platforms. With tools like ChatGPT, you can now perform these tasks efficiently, even without coding expertise.

By learning to use LLMs for text classification, you’re stepping into the future of intelligent automation.

Start mastering these skills at AI Computer Classes – Indore, where every concept is turned into hands-on practice.

📞 Contact AI Computer Classes – Indore

✉ Email: hello@aicomputerclasses.com

📱 Phone: +91 91113 33255

📍 Address: 208, Captain CS Naidu Building, near Greater Kailash Road, opposite School of Excellence For Eye, Opposite Grotto Arcade, Old Palasia, Indore, Madhya Pradesh 452018

🌐 Website: www.aicomputerclasses.com