RAG (Retrieval-Augmented Generation)

Definition

A way of giving an AI the right reference materials before asking it a question. The system searches a library of documents for the parts that look most relevant, hands them to the AI, and then asks the question. This way the answer is grounded in real, up-to-date information instead of the AI's general training.

More from AI & Prompt Engineering

• Agentic AI

  An AI that can do more than answer one question at a time. You give it a goal, and it figures out the steps, takes them, checks the results, and keeps going until the job is done: without you holding its hand at each step.

• Chain of Thought

  A trick where you ask the AI to think out loud, walking through its reasoning step by step, before giving a final answer. It sounds simple, but it dramatically improves accuracy on math problems, puzzles, and anything that needs careful logic.

• Context Files (CLAUDE.md, AGENTS.md)

  Markdown files kept at the root of a repo that describe conventions, commands, and non-negotiables for an AI coding assistant. The assistant loads them automatically, so they're where you put the rules you don't want to repeat in every prompt.

• Context Window

  How much text an AI can hold in its head at once: both what you send it and what it writes back. A bigger context window means it can read longer documents, remember more of a conversation, or look at more files at the same time.

• Few-Shot Prompting

  Showing the AI a few examples of exactly what you want before asking it to do the same for your real input. The examples teach it the pattern far more reliably than a long description ever could.

• Fine-Tuning

  Taking an AI that already knows a lot and giving it extra training on a smaller, more focused set of examples. So it gets really good at one specific job or style. Unlike just writing a clever prompt, fine-tuning actually changes how the AI thinks.