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In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance generative responses from a large language model for specific applications in that particular domain. It allows the large language models to provide more accurate, contextually relevant, and robust results, especially for targeted sectors […]
Large Multimodal Models (LMMs) are a revolution in artificial intelligence (AI). Unlike traditional AI models that operate within a single data environment such as text, images, or audio, LMMs are capable of creating and processing multiple modalities simultaneously. Hence the generation of outputs with context-aware multimedia information. The purpose of this article is to unravel
RAG (Retrieval-Augmented Generation) is a recent way to enhance LLMs in a highly effective way, combining generative power and real-time data retrieval. RAG allows a given AI-driven system to produce contextual outputs that are accurate, relevant, and enriched by data, thereby giving them an edge over pure LLMs. RAG optimization is a holistic approach that
Large Language Models (LLMs) such as GPT-4 and Llama 3 have affected the AI landscape and performed wonders ranging from customer service to content generation. However, adapting these models for specific needs usually means choosing between two powerful techniques: Retrieval-Augmented Generation (RAG) and fine-tuning. While both these approaches enhance LLMs, they are articulate towards different
Imagine you have an x-ray report and you need to understand what injuries you have. One option is you can visit a doctor which ideally you should but for some reason, if you can’t, you can use Multimodal Large Language Models (MLLMs) which will process your x-ray scan and tell you precisely what injuries you
What Are Multimodal Large Language Models? Applications, Challenges, and How They Work Read More »
Apple is working on a wearable device that will allow the user to take advantage of AI models, according to sources familiar with the product who spoke with tech publication The Information. The product is said to be “the same size as an AirTag, only slightly thicker,” and will be worn as a pin, inviting
Report: Apple plans to launch AI-powered wearable pin device as soon as 2027 Read More »
AI-företag som OpenAI, Anthropic och Google har konsekvent hävdat att deras språkmodeller inte lagrar kopior av träningsdata, utan bara ”lär sig mönster”. I inlagor till den amerikanska Copyright Office har flera företag aktivt förnekat att deras modeller fungerar som lagringssystem. Men en ny studie från Stanford och Yale, publicerad i januari 2026, slår hål på
AI-företagen ljuger: LLM-modeller har lagrat hela upphovsrättsskyddade böcker Read More »
What adversarial prompt generation means Adversarial prompt generation is the practice of designing inputs that intentionally try to make an AI system misbehave—for example, bypass a policy, leak data, or produce unsafe guidance. It’s the “crash test” mindset applied to language interfaces. A Simple Analogy (that sticks) Think of an LLM like a highly capable
Adversarial Prompt Generation: Safer LLMs with HITL Read More »
Anthropic’s agentic tool Claude Code has been an enormous hit with some software developers and hobbyists, and now the company is bringing that modality to more general office work with a new feature called Cowork. Built on the same foundations as Claude Code and baked into the macOS Claude desktop app, Cowork allows users to
Anthropic launches Cowork, a Claude Code-like for general computing Read More »
Linux and Git creator Linus Torvalds’ latest project contains code that was “basically written by vibe coding,” but you shouldn’t read that to mean that Torvalds is embracing that approach for anything and everything. Torvalds sometimes works on a small hobby projects over holiday breaks. Last year, he made guitar pedals. This year, he did
Even Linus Torvalds is trying his hand at vibe coding (but just a little) Read More »
