Tech Leaderism

Lessons from Working with LLMs

I've been actively exploring large language models (LLMs) and chatbots, particularly since the release of DeepSeek. Working with them both in the cloud and locally, I've applied them to various scenarios-from software development and finance to mentoring, data analysis, and even travel planning.

Recently, I was analyzing a very large dataset and ran into a roadblock: the file size was simply too large for the LLM to process effectively. I tried several approaches-cleaning, transforming, compressing, and even splitting the data into smaller chunks. In essence, I was adapting my problem to fit the tool.

Then, in one of my iterations, something unexpected happened. The LLM itself suggested that perhaps I was using the wrong tool for the job. And it was right. I was so focused on making the problem work within the constraints of an LLM that I overlooked more suitable solutions.

It was a classic case of "when all you have is a hammer, everything looks like a nail." This experience was a great reminder that while LLMs are incredibly powerful, they are not a one-size-fits-all solution. Choosing the right tool for the task is just as important as understanding the problem itself.