After a few months of exploring various technologies, one thing has become increasingly clear to me: large language models are reshaping how we work, live, and build software.

The Turning Point#

During my early months in grad school, I spent time studying different areas — fuzzing, software testing, generative models. But it was while experimenting with LLM-based tools that something clicked. These models aren't just academic toys. They're being used in real products, real workflows, and real businesses right now.

I'd played around with LLMs before — writing simple chatbots, testing prompt engineering, building toy projects. But looking back, that was all surface-level stuff. I hadn't really understood how these models are integrated into production systems, or how businesses are leveraging them at scale.

Going Deeper#

To bridge that gap, I've been doing a few things:

• Reading extensively — research papers on LLM applications in software engineering, industry case studies, and technical blogs from companies deploying these models
• Building prototypes — small projects that go beyond "call the API and display the result" to understand retrieval, context management, and prompt design
• Talking to people — reaching out to developers and researchers who are working with LLMs professionally

Upcoming Internship#

I'm also about to join an internship focused on business applications of generative AI. The goal is to get hands-on experience with how companies actually use these technologies — not just the technical side, but the product thinking, the user experience, and the business model.

I think this kind of practical exposure is essential. You can read all the papers you want, but there's no substitute for seeing how things work in a real-world context.

What I've Learned So Far#

A few early takeaways:

• LLMs are powerful but messy — they need careful engineering (retrieval, guardrails, evaluation) to be useful in production
• The gap between demo and product is huge — a cool ChatGPT wrapper is not a business
• Domain knowledge matters — the best LLM applications come from people who deeply understand the problem they're solving
• The field moves insanely fast — what was state-of-the-art three months ago might already be outdated

Looking Ahead#

This summer is going to be a big learning period. Between the internship and my own research, I'm hoping to build a much deeper understanding of how LLMs can be applied meaningfully.

I'll share what I learn along the way. If you're also exploring this space, feel free to reach out via email or GitHub.