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From Prompting to Understanding LLMs
Like most developers, my initial experience with LLMs was pretty straightforward. I made API calls, experimented with prompts, tweaked instructions, and tried to get better responses from the model. Most of my focus was on the application side: how to integrate an LLM, structure prompts, and use the responses to build useful features. The model… Continue reading
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Unlocking Semantic Search with Vector Embeddings
How Machines Understand Meaning — And How You Can Use It for Smarter Search 1. Introduction: Why Do We Need Embeddings? Traditional search engines rely on keyword matching. If your search query includes the words “best pizza,” it looks for documents with those exact words. But what if a document talks about “top-rated Italian food”… Continue reading
