The Model Kitchen

If you are asking what is RAG, the short version is that it is the most practical way to make a language model answer from your data instead of only its training. This guide is for builders who want a chatbot or assistant that can quote the company handbook, the product docs, or last quarter’s notes. We will walk the whole retrieve-then-generate flow, compare it with fine-tuning, and sketch a minimal build. In this guide The problem RAG solves RAG in one diagram (in words) Step 1: chunk and embed your documents Step 2: retrieve the relevant bits Step 3: generate with context RAG vs fine-tuning Where RAG goes wrong A minimal build plan FAQ Quick answer: RAG (retrieval-augmented generation) means fetching relevant snippets from your own documents and pasting them into the prompt, so the model answers from real sources instead of memory. It is the go-to pattern for accurate, up-to-date, cite-your-sources AI features. The problem RAG solves So what is RAG actually...

The context window is the single most useful concept for making sense of why a model “forgets” the start of a long chat or suddenly gets pricey. This guide is for builders who want to stop guessing and start estimating — what a token is, how the context window bounds the model’s memory, what happens when you overflow it, and how it all maps to cost. In this guide What a token is Why models count tokens, not words The context window: the model's short-term memory What happens when you run out Tokens and cost Estimating tokens quickly Practical tips to fit more in FAQ Quick answer: A token is a small chunk of text (about four characters of English), and the context window is the maximum number of tokens a model can consider at once — your input and its reply combined. Go over that limit and the oldest text falls out of view. What a token is A token is the unit a model reads and writes — usually a short chunk of text rather than a who...

Prompt engineering basics are simply the repeatable habits that get a language model to do what you actually want — and they matter far more than any secret “magic phrase.” This guide is for anyone who has typed a request, gotten mush back, and wondered what went wrong. You will learn the structure of a reliable prompt, a few techniques that measurably help, and the quiet mistakes that sabotage your results. In this guide What prompt engineering really is The anatomy of a reliable prompt Be specific: context, task, format Few-shot examples Give the model a role Ask for structure (and get it) Common mistakes A reusable prompt template FAQ Quick answer: Prompt engineering is the practice of writing clear, specific instructions — with the right context, task, and output format — so a model gives useful results consistently. It is far less about clever tricks and far more about removing ambiguity. What prompt engineering really is At its cor...

If you have ever wondered how LLMs work without wading through linear algebra, you are in the right kitchen. This guide is for developers and curious builders who want a clear mental model of large language models — enough to use them well and to debug them when they misbehave. No math and no mysticism, just the handful of ideas that make everything else click. In this guide What an LLM is, in one honest sentence Tokens: how models see text Next-token prediction, the whole trick Training vs inference Why bigger models feel smarter Why they hallucinate What they can and can't do Where to go next FAQ Quick answer: An LLM is a very large pattern-matcher trained to predict the next chunk of text — a “token” — over and over, using patterns it absorbed from huge amounts of writing. That one trick is enough to answer questions, write code, and hold a conversation, and it also explains the model’s quirks. What an LLM is, in one honest sente...

LLM hallucinations — confident, fluent answers that happen to be false — are the number-one reason teams get burned when they ship AI features. This guide is for builders who want to understand why models make things up and, more importantly, what actually reduces it. We will reframe hallucination as a predictable behavior, then work through grounding, prompting, and verification tactics that hold up in practice. In this guide What 'hallucination' means here Why it happens: prediction, not lookup When models are most likely to make things up Grounding with retrieval Asking models to say 'I don't know' Verification patterns What you can't fully fix (yet) FAQ Quick answer: Hallucinations happen because a model predicts plausible text rather than looking facts up, so it fills gaps with confident guesses. You cannot fully eliminate this, but grounding the model in real sources, letting it say “I do not know,” and verifying key claims cut it down sha...