Why write SQL queries when you can get an LLM to write the code for you? Query NFL data using querychat, a new chatbot component that works with the Shiny web framework and is compatible with R and ...

If you’ve ever tried to build a agentic RAG system that actually works well, you know the pain. You feed it some documents, cross your fingers, and hope it doesn’t hallucinate when someone asks it a ...

Abstract: With the rapid development of technologies such as smart city, digital twin, and metaverse, the data volume of three-dimensional building models is experiencing explosive growth. To address ...

As large language models (LLMs) continue to improve at writing code, a key challenge has emerged: enabling them to generate complex, high-quality training data that actually reflects real-world ...

Have you ever found yourself wrestling with Excel formulas, wishing for a more powerful tool to handle your data? Or maybe you’ve heard the buzz about Python in Excel and wondered if it’s truly the ...

Structure content for AI search so it’s easy for LLMs to cite. Use clarity, formatting, and hierarchy to improve your visibility in AI results. In the SEO world, when we talk about how to structure ...

If you’ve ever found yourself staring at a messy spreadsheet of survey data, wondering how to make sense of it all, you’re not alone. From split headers to inconsistent blanks, the challenges of ...

This repository is place where you can learn about data structures and algorithms if you want, but don't play to much with it because it's too hard.

In the vast world of artificial intelligence, developers face a common challenge – ensuring the reliability and quality of outputs generated by large language models (LLMs). The outputs, like ...