Llm

When you need an LLM to use your knowledge or behave a specific way, two approaches dominate the conversation: RAG (retrieval-augmented generation) and fine-tuning. They sound interchangeable and they’re not — they solve different problems and have very different cost, complexity, and maintenance profiles. Getting RAG vs fine-tuning right early saves you a lot of wasted GPU budget. Here’s the honest comparison. The one-line difference RAG changes what the model knows right now by injecting relevant documents into the prompt at query time. The model’s weights never change. Fine-tuning changes how the model behaves by updating its weights on your examples. Knowledge problem → reach for RAG. Behavior/format/style problem → consider fine-tuning. Most “the AI doesn’t know our stuff” issues are knowledge problems. ...

Running LLMs locally with Ollama means an open-weight model lives on your machine — no API keys, no per-token billing, no sending your data to someone else’s servers. You install Ollama, pull a model with one command, and chat from the terminal or hit a local API. For prototyping, privacy-sensitive work, or just learning how these models behave without a credit card attached, it’s the simplest on-ramp there is. Install and run a model Installation is straightforward: grab the Ollama binary for macOS, Linux, or Windows from the project site and run it. Then, from the command line: ...

Prompt engineering is just the craft of phrasing your request so the model gives you what you actually want — the right format, tone, and level of detail. It’s less “magic words” and more “clear communication with a literal-minded intern.” These prompt engineering tips work across most modern LLMs (GPT, Claude, Llama, and friends), and none of them require yelling, emojis, or threatening the model into compliance. Be explicit about task and format Vague in, vague out. “Tell me about APIs” can mean anything; the model guesses, and you get a rambling essay. Spell out task, audience, length, and format: ...

A large language model (LLM) is a neural network trained on enormous amounts of text to do one deceptively simple thing: predict the next token (roughly, the next word-piece) in a sequence. Do that well enough, at billions of parameters, and something surprising falls out — the model can answer questions, summarize documents, translate, and write code. Models like GPT, Claude, and Llama are all LLMs. This is the no-hype, human-friendly explanation of what they are and why they matter to anyone building software. ...