Prompting

A practical guide to writing prompts that produce useful, controlled outputs for real business work rather than clever toy demos.

Opening — Why this matters now Large language models are prolific. Unfortunately, they are also boring in a very specific way. Give an LLM a constrained task—generate a programming problem, write a quiz, design an exercise—and it will reliably produce something correct, polite, and eerily similar to everything it has produced before. Change the temperature, swap the model, even rotate personas, and the output still clusters around the same conceptual center. ...

In the escalating arms race between fraudsters and detection systems, recent advances in Graph-Enhanced LLMs hold enormous promise. But they face a chronic problem: too much information. Take graph-based fraud detection. It’s common to represent users and their actions as nodes and edges on a heterogeneous graph, where each node may contain rich textual data (like reviews) and structured features (like ratings). To classify whether a node (e.g., a user review) is fraudulent, models like GraphGPT or HiGPT transform local neighborhoods into long textual prompts. But here’s the catch: real-world graphs are dense. Even two hops away, the neighborhood can balloon to millions of tokens. ...