knowledge base
30 articles about knowledge base in AI news
Future-Proof Your AI Search: Why Static Knowledge Bases Fail Luxury Retail
New research reveals AI retrieval benchmarks degrade over time as information changes. For luxury brands using AI for product recommendations and clienteling, this means static knowledge bases become stale, hurting customer experience and sales.
How Anthropic's Team Uses Skills as Knowledge Containers (And What It Means For Your CLAUDE.md)
Learn how to use Claude Code skills not just for automation but as living knowledge bases, following patterns from Anthropic's own engineering team.
How This Developer Built a Production-Ready RAG System with Claude Code in One Weekend
A developer used Claude Code to create a structured JSON-to-PDF knowledge base with 105 quotes, demonstrating how to build RAG-ready datasets faster than ever.
RAG Eval Traps: When Retrieval Hides Hallucinations
A new article details 10 common evaluation pitfalls that can make RAG systems appear grounded while they are actually generating confident nonsense. This is a critical read for any team deploying RAG for customer service or internal knowledge bases.
How Non-Developers Can Use `claude --resume` and CLAUDE.md to Save Hours
Stop losing your work and repeating yourself. Use `claude --resume` to recover sessions and CLAUDE.md as a permanent knowledge base for any project.
Perplexity CEO Reveals Key Distinction Between AI Search and Traditional Models
Perplexity CEO Aravind Srinivas explains how their 'Personal Computer' approach fundamentally differs from OpenAI's models, emphasizing real-time information retrieval over static knowledge bases. This distinction highlights the evolving landscape of AI-powered search tools.
GitNexus Open Sources Codebase Knowledge Graph Engine for AI Agents
GitNexus, an open-source knowledge graph engine, autonomously indexes codebases to map dependencies and execution flows. It integrates with Claude Code, Cursor, and Windsurf via MCP to give AI agents architectural awareness, preventing breaking changes.
Claude Code Plugin 'Understand' Generates Interactive Knowledge Graphs from Codebases
A new Claude Code plugin called 'Understand' automatically analyzes any codebase to create an interactive knowledge graph. It enables developers to query code in plain English, visualize dependencies, and generate onboarding guides.
GitNexus Revolutionizes Code Exploration: Browser-Based AI Transforms GitHub Repositories into Interactive Knowledge Graphs
A new tool called GitNexus transforms any GitHub repository into an interactive knowledge graph with AI chat capabilities, running entirely in the browser without backend infrastructure. This breakthrough enables developers to visualize and query complex codebases through intuitive graph interfaces and natural language conversations.
Andrej Karpathy's Personal Knowledge Management System Uses LLM Embeddings Without RAG for 400K-Word Research Base
AI researcher Andrej Karpathy has developed a personal knowledge management system that processes 400,000 words of research notes using LLM embeddings rather than traditional RAG architecture. The system enables semantic search, summarization, and content generation directly from his Obsidian vault.
Graph-Based Recommendations for E-Commerce: A Technical Primer
An overview of how graph-based recommendation systems work, using knowledge graphs to connect users, items, and attributes for more accurate and explainable product suggestions in e-commerce.
MemFactory Framework Unifies Agent Memory Training & Inference, Reports 14.8% Gains Over Baselines
Researchers introduced MemFactory, a unified framework treating agent memory as a trainable component. It supports multiple memory paradigms and shows up to 14.8% relative improvement over baseline methods.
New Research Proposes FilterRAG and ML-FilterRAG to Defend Against Knowledge Poisoning Attacks in RAG Systems
Researchers propose two novel defense methods, FilterRAG and ML-FilterRAG, to mitigate 'PoisonedRAG' attacks where adversaries inject malicious texts into a knowledge source to manipulate an LLM's output. The defenses identify and filter adversarial content, maintaining performance close to clean RAG systems.
Microsoft's Satya Nadella Details Internal 'Lean for Knowledge Work' AI Initiative
Microsoft CEO Satya Nadella described the company's internal application of AI to streamline knowledge work, framing it as a 'Lean' manufacturing-style efficiency push for cognitive tasks. The initiative focuses on using AI to reduce process friction and improve productivity across internal operations.
DeepSeek Teases 'Much Larger' Base Model Release Amid Industry Silence and Hardware Challenges
DeepSeek staff confirmed a new, larger base model is coming soon, following months of quiet after reports of failed Huawei chip training. This comes as the Chinese AI lab faces heightened expectations after its breakthrough o1-level model in January 2025.
Knowledge-RAG v3.0: The Local RAG MCP Server That Finally Just Works
Knowledge-RAG v3.0 eliminates Docker/Ollama setup, adds hybrid search with cross-encoder reranking, and auto-indexes your docs—making private RAG in Claude Code a one-command install.
EISAM: A New Optimization Framework to Address Long-Tail Bias in LLM-Based Recommender Systems
New research identifies two types of long-tail bias in LLM-based recommenders and proposes EISAM, an efficient optimization method to improve performance on tail items while maintaining overall quality. This addresses a critical fairness and discovery challenge in modern AI-powered recommendation.
New Research Diagnoses LLMs' Struggle with Multiple Knowledge Updates in Context
A new arXiv paper reveals a persistent bias in LLMs when facts are updated multiple times within a long context. Models increasingly favor the earliest version, failing to track the latest state—a critical flaw for dynamic knowledge tasks.
Teaching AI to Forget: How Reasoning-Based Unlearning Could Revolutionize LLM Safety
Researchers propose a novel 'targeted reasoning unlearning' method that enables large language models to selectively forget specific knowledge while preserving general capabilities. This approach addresses critical safety, copyright, and privacy concerns in AI systems through explainable reasoning processes.
Base44 Superagent Emerges as Streamlined AI Assistant Platform
Base44 has launched Superagent, an AI assistant platform that reportedly enables users to connect to multiple productivity tools—including Gmail, Calendar, Slack, WhatsApp, and Telegram—in under three minutes. Early users praise its clean setup experience and direct integration capabilities.
Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025
A keynote at ECIR 2025 will present research on how Large Language Models (LLMs) balance their internal, parametric knowledge with external, contextual information. This is critical for deploying reliable AI in knowledge-intensive tasks where models must correctly use provided context, not just their training data.
Reinforcement Learning Ushers in New Era of Autonomous Knowledge Agents
Researchers are developing knowledge agents powered by reinforcement learning that can autonomously gather, process, and apply information. These systems represent a significant evolution beyond traditional language models toward more independent problem-solving capabilities.
LLM-Based Multi-Agent System Automates New Product Concept Evaluation
Researchers propose an automated system using eight specialized AI agents to evaluate product concepts on technical and market feasibility. The system uses RAG and real-time search for evidence-based deliberation, showing results consistent with senior experts in a monitor case study.
EpisTwin: A Neuro-Symbolic Framework for Personal AI Using Knowledge Graphs
Researchers propose EpisTwin, a neuro-symbolic architecture that builds a Personal Knowledge Graph from fragmented user data to enable complex, verifiable reasoning. It addresses limitations of standard RAG by capturing semantic topology and temporal dependencies.
Beyond Vector Search: How Core-Based GraphRAG Unlocks Deeper Customer Intelligence for Luxury Brands
A new GraphRAG method using k-core decomposition creates deterministic, hierarchical knowledge graphs from customer data. This enables superior 'global sensemaking'—connecting disparate insights across reviews, transcripts, and CRM notes to build a unified, actionable view of the client and market.
Multimodal Knowledge Graphs Unlock Next-Generation AI Training Data
Researchers have developed MMKG-RDS, a novel framework that synthesizes high-quality reasoning training data by mining multimodal knowledge graphs. The system addresses critical limitations in existing data synthesis methods and improves model reasoning accuracy by 9.2% with minimal training samples.
Graph-Based AI Agents Are Revolutionizing Software Development
Researchers are developing graph-based multi-agent systems that dynamically adapt their collaboration patterns to solve complex coding problems more effectively than traditional fixed architectures.
Beyond Vector Databases: New RAG Approach Achieves 98.7% Accuracy Without Embeddings or Similarity Search
Researchers have developed a novel RAG method that eliminates vector databases, embeddings, chunking, and similarity searches while achieving state-of-the-art 98.7% accuracy on financial benchmarks. This approach fundamentally rethinks how AI systems retrieve and process information.
Fenty Beauty Launches 'Rose Amber' AI Advisor on WhatsApp, Joining L'Oréal in Chat-Based Commerce Push
Fenty Beauty has launched 'Rose Amber,' a conversational AI advisor on WhatsApp for product recommendations and tutorials. This reflects a broader industry shift, with L'Oréal already generating over 20% of its DTC sales in Brazil via WhatsApp and planning a 2026 expansion of its own AI tool to the platform.
Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?
New research warns that RAG systems can be gamed to achieve near-perfect evaluation scores if they have access to the evaluation criteria, creating a risk of mistaking metric overfitting for genuine progress. This highlights a critical vulnerability in the dominant LLM-judge evaluation paradigm.