Llms
9 articles
Foundation Models Explained: The Adaptable AI Engines That Go Far Beyond Text
Foundation models are a new paradigm in AI, representing large, adaptable engines trained on broad data that can be applied to diverse tasks across modalities, not just text.
How Knowledge Graphs Make AI Answers More Accurate
This article explains how integrating knowledge graphs into Retrieval-Augmented Generation (RAG) systems significantly enhances AI's factual accuracy and reasoning capabilities. By moving beyond unstructured text, knowledge graphs provide a structured foundation that reduces AI hallucinations and improves reliability.
What Are Attention Mechanisms and Why Do They Power Transformers?
A single Transformer model, built exclusively on attention mechanisms, achieved a 41.
What Are Vector Databases and How Do They Power AI Applications?
Retrieval-Augmented Generation (RAG) is the primary use case driving vector database adoption in 2026.
How to Build AI Applications with LangChain
Over 63% of mid-sized companies (100-2000 employees) now run AI agents in production, reports LangChain .
Positional Encoding for LLMs: Challenges and ADAT's Solution
A novel Adaptive Transformer (ADAT) architecture has demonstrated superior translation accuracy and faster training times on complex datasets like MedASL, offering a clearer window into nuanced commun
Building Multi-Agent Systems with Python Powers Next-Gen AI
Llama 4 Scout achieved an 82% win rate against human players in 'Battleship' after strategic inference refinements, outperforming GPT-5 at a reduced cost, according to MIT News .
Proxy-Pointer RAG builds accurate, private knowledge graphs
A new AI framework builds highly accurate knowledge graphs from sensitive data, such as exercise records, without compromising privacy.
Why Stop Using LLMs as Giant Problem Solvers
Processing just 10,000 customer support tickets with a top-tier large language model (LLM) can cost roughly $16, according to pecollective.