Applied Software Engineering Case Diary: Field Lessons in Problem Solving - Gireesh Punathil

As AI-powered applications become more user-friendly, the ability to translate natural language into SQL is transforming database interactions. This session explores how AI interprets everyday language and converts it into precise, efficient, and secure SQL queries. Whether you're designing self-service analytics platforms or enhancing enterprise applications with natural language search, this talk will equip you with practical techniques to build intelligent query interfaces.

Through real-world examples and interactive demonstrations, you’ll learn how NLP and machine learning bridge the gap between human intent and structured data retrieval. We'll also dive into best practices for query optimization, security considerations, and effective result presentation—whether in text, tables, or dynamic visualizations.

Key Takeaways

Efficient Query Translation – Understand how NLP and AI process user intent to generate SQL queries that are both accurate and performant.
System Design Insights – Learn principles for structuring AI-powered query systems, including handling dates, trends, and complex aggregations.
Effective Result Presentation – Explore strategies to display query results in intuitive formats—text, tables, or visualizations—tailored to end-user expectations.
Similarity Search Techniques – Gain insights into mapping user prompts to relevant SQL queries using vector search and embedding-based similarity models.
Robust Implementation Practices – Identify common pitfalls and best practices to ensure secure, scalable, and maintainable deployments in real-world applications.

Target Audience

Developers & Engineers – Software engineers building AI-driven database query interfaces or integrating natural language search into applications.
AI & NLP Enthusiasts – Researchers and practitioners exploring language models for structured data interactions.
Data Scientists & Analysts – Professionals working with self-service analytics, BI platforms, and conversational AI for querying databases.
Educators & Students – Academics and learners interested in the intersection of AI, NLP, and database systems.

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