Small Language Models: Why Smaller Is Suddenly Smarter
The race is no longer only about bigger models. Small language models that run cheaply — even on a phone — are one of AI's most important trends. Here is why, and what to watch.
#llm
13 articles tagged with “llm”.
Multimodal AI Goes Mainstream: One Model, Many Senses
The leading AI models no longer just read and write text — they see images and hear audio too. Here is what 'multimodal' means and why it has become the default expectation.
Open vs Closed AI Models: Which Should You Build On?
Should you build on an open-weight model you run yourself, or a closed model behind an API? A practical comparison across control, cost, capability, privacy, and effort.
What Is RAG? Retrieval-Augmented Generation Explained Simply
RAG is the technique that lets a language model answer using your own documents instead of only what it memorized in training. Here is how it works and why almost every serious AI app uses it.
What Is Fine-Tuning? Customizing AI Models, Explained
Fine-tuning takes a general-purpose AI model and specializes it for your task, tone, or format by training it further on your examples. Here is when it helps — and when prompting or RAG is the better tool.
Prompt Engineering Basics: How to Get Better Answers From AI
Most people get mediocre answers from AI because of vague prompts. A few simple techniques — being specific, giving examples, and asking for step-by-step reasoning — reliably improve results.
Weekly AI Roundup: Agents, Local Models, and the Regulation Clock
Our inaugural weekly roundup: why agents dominate the conversation, what the local-model ecosystem's maturity means for builders, and the regulatory deadlines coming into focus.
Ollama: Run Open-Source LLMs on Your Own Machine in Minutes
Ollama turned 'running an LLM locally' from a weekend project into a single command. Here is what it does well, where it hits limits, and when local models beat cloud APIs.
BERT: The Paper That Taught Machines to Read Both Directions
Before GPT dominated headlines, BERT showed that pretraining a Transformer to read text in both directions could transform language understanding. Here is what the landmark 2018 paper introduced.
GPT-3: The Paper That Showed Scale Creates New Abilities
The 2020 GPT-3 paper made a startling claim: make a language model big enough and it learns new tasks from a few examples in the prompt, with no retraining. Here is what it showed.
LangChain: The Framework That Defined AI Application Orchestration
LangChain is the open-source framework that popularized chaining LLM calls, retrieval, and tools into full applications. Here is what it offers, and the trade-offs to weigh before building on it.
Attention Is All You Need: The Paper Behind Every Modern AI Model
Every modern language model — GPT, Claude, Gemini, Llama — descends from one 2017 paper. Here is what 'Attention Is All You Need' actually proposed, in plain English, and why it changed everything.
What Is a Large Language Model? A Plain-English Guide
LLMs power ChatGPT, Claude, and Gemini — but what are they, really? This beginner guide explains how they work, what they're good and bad at, and the vocabulary you need, with zero math.