Aditya Gupta

Chain-of-thought prompting asks a large language model (LLM) to work through intermediate steps before answering, while zero-shot prompting gives it a task without examples. The useful choice depends on the model and the task because current reasoning models often reason…

Code embeddings turn functions, classes, comments, and natural-language questions into vectors that an artificial intelligence (AI) assistant can compare to retrieve code by meaning when filenames and exact keywords do not match the question. Keyword search and semantic search solve…

ChatGPT Free, Go, and Plus fit different workloads: Free covers occasional questions and small tasks, Go gives a lower-cost paid step with more room for everyday use, and Plus suits work that regularly reaches plan limits or needs broader access…

ChatGPT can help you turn a job description, your verified experience, and a clear target role into a stronger application package. The useful outcome is a draft you can inspect against your own work history, not text you submit without…

ChatGPT prompt templates give you a reusable starting point when an open-ended question produces a vague answer. The useful ingredients are a goal, the context ChatGPT needs, constraints, and a clear output format. Build a useful prompt before you use…

ChatGPT can help you move from a rough topic to a usable draft, but the result depends on the instruction you give it. I use prompts like the ones below when I want ideas, titles, outlines, SEO checks, and cleaner…

Google still works well when I know the exact site or phrase I need, but Perplexity AI feels better when the question needs search, source checking, and a short answer in one place. My Perplexity AI all about guide focuses…

AI CLI agents put a pair programmer inside your terminal. They understand natural language, write code from a sentence, and run the same shell commands you already know. This is a working reference for the most useful Claude Code, Aider,…