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

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

Artificial Intelligence is evolving rapidly, and Large Language Models (LLMs) like ChatGPT have redefined how we handle text data. From analyzing customer feedback to categorizing documents, text classification is a crucial skill for anyone working with natural language.

In this practical guide from AI Computer Classes – Indore, we’ll explore how you can use LLMs for text classification, understand their working, and learn tips and tricks to make the process more accurate and efficient — all through hands-on ChatGPT examples.

🚀 What is Text Classification?

Text classification is the process of automatically labeling text into predefined categories.

💡 Examples:

• Classifying emails as Spam or Not Spam
• Categorizing news articles by topic (Sports, Politics, Tech)
• Sentiment analysis: Positive, Negative, or Neutral
• Sorting resumes by job roles

In traditional machine learning, this required feature extraction + model training, but with LLMs like ChatGPT, you can now perform classification directly using prompts and minimal coding!

💡 Learn Practically at AI Computer Classes – Indore!

Gain hands-on skills in AI, Machine Learning, and ChatGPT-based automation.

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🧩 Understanding LLMs and How They Classify Text

LLMs such as ChatGPT, GPT-4, or Gemini are trained on vast text data. They understand context, tone, and intent, which allows them to classify text intelligently.

🧠 How LLMs Work for Classification

1. Input: You provide text and category options.
2. Processing: The LLM analyzes meaning and patterns.
3. Output: The model returns the most appropriate label.

For example, if you input:

“The customer service was quick and friendly.”

and ask:

“Classify this as Positive, Neutral, or Negative.”

ChatGPT instantly replies:

“Positive.”

No need to train or preprocess — LLMs handle it all internally!

⚙️ Using ChatGPT for Text Classification

Here’s how you can perform classification using ChatGPT prompts:

✅ Step 1: Define Categories

Clearly specify what categories you want.

Example:

“Classify the following customer reviews as Positive, Neutral, or Negative.”

✅ Step 2: Provide Examples (Few-Shot Learning)

Help ChatGPT learn your context by showing examples.

Example 1: “The food was amazing.” → Positive  
Example 2: “It was okay, not great.” → Neutral  
Example 3: “I didn’t like the service.” → Negative  

Now add your test input:

“The product arrived late.”

ChatGPT → Negative

✅ Step 3: Use Python + OpenAI API for Automation

If you’re building a scalable app, integrate LLM classification using Python.

from openai import OpenAI
client = OpenAI()

prompt = """
Classify each review as Positive, Negative, or Neutral:
1. The delivery was late.
2. Amazing service experience.
3. It’s okay, could be better.
"""

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}]
)

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

🔥 At AI Computer Classes – Indore, we teach LLM integration hands-on using Python and Excel automation tools.

📊 Example: Classifying Excel Data using ChatGPT

Yes — you can even combine ChatGPT + Excel for simple classification tasks!

Steps:

1. Export your text data (like customer feedback) into Excel.
2. Use an OpenAI Excel plugin or VBA script to call ChatGPT API.
3. Pass each text row to ChatGPT with category options.
4. Display the returned classification in a new column.

This allows you to label thousands of reviews or survey responses in minutes — no coding required!

🧠 Prompt Engineering Tips for Better Results

To get high accuracy in classification tasks, your prompt quality matters most.

💡 Tips & Tricks:

1. Be specific with categories.
2. Instead of “Good” vs “Bad”, use clear sentiment labels like Positive / Neutral / Negative.
3. Add few-shot examples.
4. Teach ChatGPT with 2–3 labeled samples.
5. Request JSON output.
6. Useful for automation and Power BI integration.
7. Example:

Return your answer as JSON: {"sentiment": "Positive"}

1. Use consistent phrasing.
2. Avoid switching category wording midway.
3. Test with diverse inputs.
4. Helps identify edge cases or ambiguity.

💡 Hands-on Practice:

Join the AI + ChatGPT Automation course at AI Computer Classes – Indore to master practical prompt engineering and API-based AI workflows!

🧩 Real-World Applications of LLM-based Text Classification

ApplicationExampleTools UsedCustomer SupportAnalyze support chats for toneChatGPT, Power BIHR AutomationSort resumes by departmentPython, ExcelMarket ResearchClassify survey responsesChatGPT, Figma dashboardsEducationCategorize student feedbackExcel + AI APIFinanceDetect risk-related keywords in documentsPython NLP

AI Computer Classes integrates tool-based exercises so learners don’t just read — they build and test real solutions.

🧩 Bonus: Combining ChatGPT with Python for Custom AI Models

While ChatGPT can classify text instantly, sometimes you may want full control. In that case, combine:

• Python (Pandas, Scikit-learn) for preprocessing
• LLMs (ChatGPT) for contextual understanding
• Power BI for visual dashboards

Example workflow taught at AI Computer Classes:

1. Clean your text data in Excel.
2. Use Python to tokenize or vectorize (optional).
3. Send text to ChatGPT for contextual classification.
4. Display summarized insights in Power BI.

This hybrid approach gives both automation and interpretability.

🧭 Practical Exercise

Try this quick challenge at home or in your AI Computer Classes lab:

1. Take 10 short movie reviews.
2. Define categories: Positive, Neutral, Negative.
3. Use ChatGPT to classify each review.
4. Compare ChatGPT’s results with a simple Python classifier (like LogisticRegression).
5. Note where ChatGPT performs better — and why.

✅ You’ll instantly understand how LLMs excel in context-driven classification.

🧠 Final Thoughts

Text classification is at the heart of many modern AI applications — and LLMs like ChatGPT make it faster, easier, and smarter than ever before.

By mastering this skill, you can automate document labeling, improve analytics dashboards, and even enhance customer experiences — all with minimal coding.

At AI Computer Classes – Indore, our practical AI programs help you apply these techniques with real projects, guided mentorship, and certification.

🚀 Start learning today — and unlock the future of intelligent text automation!

📞 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, Old Palasia, Indore, Madhya Pradesh 452018

🌐 Website: www.aicomputerclasses.com