This project focuses on building an intelligent enterprise knowledge assistant powered by a large language model integrated with internal documents and structured data. The system uses embeddings and a retrieval-augmented generation pipeline to fetch relevant context from a vector database and generate accurate, grounded responses. Prompt engineering, hallucination reduction, and response evaluation techniques are applied to ensure reliability, making the assistant suitable for corporate knowledge management and decision support systems.

This project involves fine-tuning a pre-trained large language model on a domain-specific dataset using parameter-efficient tuning techniques such as LoRA. The solution demonstrates dataset preparation, supervised fine-tuning, evaluation, and deployment for specialized use cases like legal document analysis or medical text summarization, reflecting real-world customization workflows used in industry.

This project builds an automated customer support platform using an LLM to handle FAQs, ticket classification, and response generation. The system integrates prompt templates, confidence scoring, and fallback mechanisms to human agents, ensuring accuracy and safety. It mirrors production-grade conversational AI systems deployed by enterprise SaaS organizations.