Browse all content tagged with LLMs
Agentic AI is an advanced branch of artificial intelligence that empowers systems to act autonomously, make decisions, and accomplish complex tasks with minimal human oversight. Unlike traditional AI, agentic systems analyze data, adapt to dynamic environments, and execute multi-step processes with autonomy and efficiency.
AI technology trends encompass current and emerging advancements in artificial intelligence, including machine learning, large language models, multimodal capabilities, and generative AI, shaping industries and influencing future technological developments.
Explore autonomous vehicles—self-driving cars that use AI, sensors, and connectivity to operate without human input. Learn about their key technologies, AI’s role, LLM integration, challenges, and the future of smart transportation.
We've tested and ranked the writing capabilities of 5 popular models available in FlowHunt to find the best LLM for content writing.
Model fine-tuning adapts pre-trained models for new tasks by making minor adjustments, reducing data and resource needs. Learn how fine-tuning leverages transfer learning, different techniques, best practices, and evaluation metrics to efficiently improve model performance in NLP, computer vision, and more.
Language detection in large language models (LLMs) is the process by which these models identify the language of input text, enabling accurate processing for multilingual applications like chatbots, translation, and content moderation.
A metaprompt in artificial intelligence is a high-level instruction designed to generate or improve other prompts for large language models (LLMs), enhancing AI outputs, automating tasks, and improving multi-step reasoning in chatbots and automation workflows.
Model Chaining is a machine learning technique where multiple models are linked sequentially, with each model’s output serving as the next model’s input. This approach improves modularity, flexibility, and scalability for complex tasks in AI, LLMs, and enterprise applications.
Recursive prompting is an AI technique used with large language models like GPT-4, enabling users to iteratively refine outputs through back-and-forth dialogue for higher-quality and more accurate results.
Retrieval Augmented Generation (RAG) is an advanced AI framework that combines traditional information retrieval systems with generative large language models (LLMs), enabling AI to generate text that is more accurate, current, and contextually relevant by integrating external knowledge.
Text summarization is an essential AI process that distills lengthy documents into concise summaries, preserving key information and meaning. Leveraging Large Language Models like GPT-4 and BERT, it enables efficient management and comprehension of vast digital content through abstractive, extractive, and hybrid methods.
