- Data Collection and Preparation: - Scraped and cleaned over 10,000 data points from various sources to build a diverse and high-quality dataset. - Impact: Improved chatbot response accuracy by 25%, ensuring reliable and relevant outputs.- Model Fine-Tuning: - Fine-tuned a pre-trained LLM on a custom dataset using 3 personality profiles to adapt responses dynamically. - Impact: Enhanced user engagement by providing responses tailored to customer preferences and… Show more - Data Collection and Preparation: - Scraped and cleaned over 10,000 data points from various sources to build a diverse and high-quality dataset. - Impact: Improved chatbot response accuracy by 25%, ensuring reliable and relevant outputs.- Model Fine-Tuning: - Fine-tuned a pre-trained LLM on a custom dataset using 3 personality profiles to adapt responses dynamically. - Impact: Enhanced user engagement by providing responses tailored to customer preferences and needs.- Pipeline Implementation: - Developed a retrieval-augmented generation (RAG) pipeline with a vector database for embedding similarity and dynamic document retrieval. - Impact: Boosted the chatbot's information retrieval precision by 30%, enabling faster and context-aware responses.- Platform Development: - Built an interactive user interface, enabling clients to upload documents, customize chatbot attributes (e.g., interests, previous issues), and select preferred models. - Impact: Increased user satisfaction by offering flexible and personalized interaction options, driving 40% more task completions. Show less

- Data Cleaning and Preparation: Conducted extensive data cleaning to ensure high data quality.- Entity Labeling and Data Generation: Labeled relevant entities and generated additional data to enrich the training dataset.- Model Training and Evaluation: Trained several models and evaluated their performance to optimize accuracy.