Retrieval-Augmented Generation (RAG) System

This is an ongoing Project with Proshort, Bengaluru

Developing end-to-end machine learning pipelines for a Retrieval-Augmented Generation (RAG) system focused on sales enablement.

Implemented custom algorithms to extract relevant metadata from un-structured/semi-structured data sources, boosting the retrieval recall and enhancing the overall performance of the RAG system.

Designed and implemented agentic workflows to orchestrate large language models, enabling efficient execution of various natural language processing tasks, including text generation, summarization, and question-answering.

Collaborating closely with cross-functional teams, including data engineers, subject matter experts, and product managers, to ensure the successful deployment and integration of the RAG system into the company's sales enablement platform.