GENERATIVE AI - Natural Language Processing
1. Financial Complaint Classification & Summarization using Generative AI and Large Language Models
Description
Developed an end-to-end Generative AI solution for automating financial product complaint classification, customer complaint summarization, and AI-assisted response generation using Large Language Models (LLMs) and Transformer-based NLP. The project involved data preprocessing, fine-tuning a BERT model for multi-class text classification, implementing Prompt Engineering with the Mistral 7B Instruct model for zero-shot and few-shot learning, and evaluating model performance using industry-standard NLP metrics. The solution enables financial institutions to automatically categorize customer complaints, generate concise summaries for mediation teams, and support faster, more personalized customer service.
Key Features
- Generative AI for Financial Complaint Analysis
- Large Language Models (LLMs)
- BERT Fine-Tuning for Text Classification
- Prompt Engineering (Zero-Shot & Few-Shot Learning)
- Mistral 7B Instruct Integration
- Customer Complaint Summarization
- Automated Product Classification
- Sentiment-Aware Response Generation
- Transformer-Based Natural Language Processing (NLP)
- Model Evaluation using F1-Score, Precision, Recall & Accuracy
Business Impact
Demonstrates how Generative AI and Large Language Models can automate customer complaint processing, reduce manual effort, improve classification accuracy, generate concise complaint summaries, and accelerate customer support workflows. The solution enhances operational efficiency, regulatory compliance, and customer experience in the financial services sector by enabling faster and more consistent complaint handling.
Technologies Used
Python • Generative AI • Large Language Models (LLMs) • BERT • Mistral 7B Instruct • Hugging Face Transformers • TensorFlow • Prompt Engineering • Natural Language Processing (NLP) • llama.cpp • Hugging Face Hub • Scikit-learn • Pandas • NumPy • Google Colab • Jupyter Notebook
2. Customer Review Classification & Summarization using Generative AI and Large Language Models
Description
Designed and developed an end-to-end Generative AI solution to automate customer review classification, aspect-based sentiment analysis, and intelligent review summarization using Large Language Models (LLMs). The project leveraged Llama 2 (13B) with Prompt Engineering, Few-Shot Learning, and Parameter-Efficient Fine-Tuning (PEFT) to classify product reviews, identify sentiment for individual product aspects, and generate concise summaries. The solution demonstrates how open-source LLMs can enhance customer feedback analysis and support business decision-making through scalable AI-powered Natural Language Processing.
Key Features
- Generative AI-powered Customer Review Analytics
- Large Language Models (Llama 2–13B)
- Aspect-Based Sentiment Analysis (ABSA)
- Prompt Engineering (Zero-Shot & Few-Shot Learning)
- Parameter-Efficient Fine-Tuning (PEFT)
- Customer Review Summarization
- Multi-Class Product Classification
- Transformer-Based Natural Language Processing
- Automated Sentiment & Aspect Extraction
- Model Evaluation using Precision, Recall & F1-Score
Business Impact
Built an AI-powered customer feedback analysis system that automatically classifies product reviews, identifies sentiment for specific product features, and generates concise summaries for business stakeholders. The solution helps organizations understand customer opinions more efficiently, improve products based on aspect-level insights, reduce manual review effort, and accelerate data-driven decision-making in e-commerce and customer support.
Technologies Used
Python • Generative AI • Large Language Models (LLMs) • Llama 2–13B • Prompt Engineering • Few-Shot Learning • Parameter-Efficient Fine-Tuning (PEFT) • Hugging Face • llama.cpp • Transformers • PyTorch • Datasets • Pandas • NumPy • Scikit-learn • Natural Language Processing (NLP) • Aspect-Based Sentiment Analysis (ABSA) • Google Colab • Jupyter Notebook