Prakash Mandayam Comar

Automated construction of shopping cart from medical prescriptions is a vital prerequisite for scaling up online pharmaceutical services in emerging markets due to the high prevalence of paper prescriptions that are challenging for customers to interpret.

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions to Structured Query Language (SQL) queries. With advancements in Large Language Models (LLM), in-context learning (ICL) has emerged as a popular choice for building Text-to-SQL systems. Real world, industry-scale databases, often comprise thousands of tables and hundreds of columns, and makes passing the entire schema as context to an LLM infeasibly expensive.

Traditional Query Auto-completion (QAC) systems optimise for query relevance based on past user interactions. This approach excels at surfacing frequently searched queries, but ensuring a diverse range of suggestions and incorporating new products or trends often requires post-processing heuristics. This limitation stems from relying on user search logs, which may not fully capture the evolving product landscape.