Introduction to Information Retrieval

In 2005, I began working in Information Retrieval (IR) and Data Mining, which is the foundational technology behind many AI applications, especially search, recommendation, and retrieval-augmented generation (RAG). As IR rapidly grew and became more widely applied, I began offering an elective course for undergraduate computer science students called Introduction to Information Retrieval. The goal of the course wasn’t just to teach IR techniques, but to help students think critically and learn how to design complex, real-world systems. I’ve always seen IR as a powerful intersection of applied computer science and software engineering.

Now, with the rapid growth of AI and the rise of RAG, it is worth revisiting the fundamentals of IR and highlighting why they matter more than ever. It is important for computer science and engineering programs to keep their curricula aligned with the skills future graduates need, so software engineers remain relevant and effective in a rapidly evolving field.

Is the Senior Developer Still Relevant in the Age of AI? A Reflection from the Field

Over the past two months, I worked extensively with generative AI (Claude Code) to build a complex enterprise-grade system, from simple data retrieval and UI interactions to an integrated data mart and backend infrastructure. This was not a static or well-defined task. In fact, the project was intentionally designed to be Agile: the requirements changed rapidly, the priorities shifted frequently, and iterative feedback was an important part of the process. AI was embedded in the development workflow at every step — not just as a code generator, but as a partner.