Tag: LLM

Generative AI is evolving towards agentic capabilities, where AI systems can autonomously plan, utilize tools, and collaborate to achieve complex goals. This shift signifies a move from content creation to intelligent action, impacting various industries and redefining human-AI interaction.

Mistral AI has unveiled Mixtral 8x22B, a groundbreaking open-source large language model that offers exceptional coding and mathematical capabilities, alongside multilingual fluency, all while maintaining remarkable cost-efficiency. This deep dive explores its architecture, performance, and implications for the AI landscape.

This guide breaks down Microsoft's 12-lesson course on building AI agents, covering foundational concepts, design patterns, frameworks, and production readiness. It's an instructional roadmap for beginners to master agentic AI development.

Explore AutoGen, a Microsoft-developed framework that simplifies LLM application development through customizable, conversable agents. Discover how multi-agent conversations, flexible programming, and diverse applications are shaping the future of AI.

This tutorial explores the architecture, tools, and economics of building an AI-powered Slack agent that leverages Agentic Retrieval-Augmented Generation (RAG) to access and synthesize company knowledge, aiming to significantly reduce information retrieval time for employees.

This tutorial explores the implementation of a JSON-based agent using Ollama and LangChain to interact with a Neo4j graph database, enhancing LLM capabilities with tools and a semantic layer.

Discover how to leverage the full potential of the LangChain ecosystem, from core components to deployment, for building robust AI applications.

Explore how Retrieval Augmented Generation (RAG) and Fine-Tuning can significantly enhance the accuracy and relevance of Large Language Models (LLMs). This tutorial details their mechanisms, differences, and when to apply each technique for optimal performance.

This tutorial demonstrates how to build a configurable Retrieval Augmented Generation (RAG) system using a modular approach with Haystack and Hypster. It covers setting up LLM configurations, indexing pipelines with optional document enrichment, and flexible retrieval pipelines supporting both BM25 and embedding-based retrieval methods.

Discover how WebExplorer is pioneering a new era in training sophisticated web agents by generating its own high-quality, complex training data, eliminating the need for human-labeled examples and achieving state-of-the-art performance.

Arize AI and LlamaIndex have launched LlamaTrace, a joint platform aimed at simplifying the evaluation and observability of Large Language Model (LLM) applications. This collaboration leverages open-source foundations to provide a hosted solution for developers, addressing key challenges in deploying sophisticated AI systems.

This article explores Retrieval Augmented Generation (RAG), Agent+RAG, and evaluation techniques using TruLens. It demonstrates how to build custom data retrieval systems for LLMs to overcome limitations in detail and knowledge recency, using LlamaIndex and Neo4j, and benchmarks different LLM approaches.

Explore LlamaIndex, a powerful data framework that simplifies integrating private and domain-specific data with Large Language Models (LLMs) for advanced AI applications. Discover its core components, workflow, and use cases in this comprehensive analysis.

Explore the capabilities of Gemini 2.0, focusing on Gemini 2.0 Flash, and learn to build a document Q&A application with memory using the LlamaIndex framework and a RAG chatbot.

New research from NYU Stern and Goodfin indicates that advanced AI language models can now pass the CFA Level III exam, a significant milestone in AI

DeepSeek-V3.1-Terminus, the latest iteration of DeepSeek AI's hybrid reasoning model, has been launched with significant enhancements in agentic tool use and a marked reduction in language mixing errors. This update promises more reliable and efficient AI interactions for developers and end-users, building upon the strengths of its predecessor while addressing key user feedback.

Discover how NVIDIA NIMs simplify the integration of Mistral and Mixtral models into production environments. This guide provides an instructional overview of their benefits, performance enhancements, and deployment strategies for AI projects.

Nigeria has launched N-ATLAS V1, its first open-source, multilingual, and multimodal large language model, at the UNGA80. Developed to digitize linguistic heritage and foster inclusive AI, N-ATLAS V1 supports Yoruba, Hausa, Igbo, and Nigerian-accented English, marking a significant step towards African leadership in AI development.

This tutorial explores the NVIDIA AI Blueprint for Video Search and Summarization (VSS), detailing its features for advanced video analytics. Learn how to leverage its capabilities for enhanced video understanding, search, and summarization through a step-by-step instructional approach.

This article provides an in-depth, analytical comparison of Meta's Llama and OpenAI's ChatGPT, evaluating their performance across creative writing, coding, image generation, and analysis tasks to guide users in choosing the right AI model for their specific needs.

Explore Mistral AI's groundbreaking Mixtral 8x7B model, a sparse Mixture of Experts (SMoE) architecture that redefines efficiency and performance in large language models. Discover how SMoE enables unprecedented capabilities while maintaining cost-effectiveness, and understand its impact on the future of AI.

This tutorial details the LLM-AIx pipeline, an open-source solution for extracting structured clinical information from unstructured oncology text using privacy-preserving large language models. It requires no programming skills and runs on local infrastructure, making it accessible for clinical research and decision-making.

Explore TAID, a groundbreaking technique by Sakana AI that efficiently transfers knowledge from large language models (LLMs) to smaller ones, overcoming the limitations of traditional methods and paving the way for more accessible and powerful AI.

This article explores the use of Large Language Models (LLMs) as automated judges for evaluating security risks in LLM-generated content. It details Trend Micro

This guide provides financial institutions with a practical framework for selecting, deploying, and governing Large Language Models (LLMs) and Small Language Models (SLMs) in 2025. It analyzes the trade-offs between model sizes based on regulatory requirements, cost, latency, security, and specific use cases, advocating for a strategic, often hybrid, approach to AI adoption.

Explore the performance benchmarks of AMD's Ryzen AI Max+ "Strix Halo" processors when utilizing the ROCm 7.0 compute stack on Ubuntu Linux. This article details the setup, testing methodology, and performance results across various AI and compute workloads.

Explore how Lloyds Banking Group is pioneering the use of AI agents as judges within Generative AI workflows to ensure accuracy, compliance, and scalability in financial guidance. This tutorial details their innovative 'agent-as-judge' approach, the benefits of specialized AI models, and practical implementation strategies for regulated industries.

Large Language Models (LLMs) possess an unprecedented ability to synthesize vast amounts of public information, potentially reconstructing "forbidden knowledge" – information that, while not classified, is dangerous if assembled. This analysis explores the mechanisms, risks, and societal implications of this capability, drawing parallels to historical precedents and examining potential future safeguards.

This article provides an in-depth tutorial-style survey of how Large Language Models (LLMs) are revolutionizing task planning. It explores the theoretical foundations, categorizes current methodologies into external module augmentation, fine-tuning, and search-based approaches, and discusses evaluation frameworks. The survey also delves into the underlying mechanisms of LLM-based planning and highlights future research directions, offering a valuable resource for the AI community.

This report details the 5th edition of Andreessen Horowitz's Top 100 Gen AI Consumer Apps, analyzing two and a half years of data to understand the evolving trends in everyday AI usage. It highlights Google's growing presence, the intensifying competition among LLM assistants, the significant role of Chinese-developed apps, the emergence of "vibe coding" platforms, and the consistent performance of "All Stars" in the generative AI space.

Meta's introduction of small reasoning models signifies a pivotal industry trend towards efficient, on-device AI for enterprise applications. This shift prioritizes specialized performance, cost-effectiveness, and enhanced privacy over sheer model size.

Discover why relying solely on public LLM benchmarks is flawed and learn how to create your own robust internal benchmarks to accurately assess models for your specific use cases. This guide provides a step-by-step approach to developing and implementing effective internal LLM evaluation strategies.

OpenAI has launched its highly anticipated GPT-5 model, boasting 'Ph.D level intelligence' and significant advancements in reasoning, coding, and accuracy. The new model aims to provide expert-level assistance to a broad user base, with tiered access for free and paid subscribers.