#RAG

Discover CLaRa, a revolutionary framework enhancing retrieval-augmented generation with novel document compression techniques.

'seekdb unifies vector, full text and relational search in a single MySQL-compatible engine, enabling RAG and agent workflows to run hybrid retrieval and in-database AI functions with one SQL query.'

'A step-by-step guide showing how persistent memory, decay, and simple retrieval turn a chatbot into a personalized agent; includes full Python demo and evaluation.'

'Hands-on tutorial showing how to build a Colab-based enterprise AI assistant using open-source models and FAISS for retrieval, including PII redaction and policy enforcement.'

'Production AI agents depend far more on data plumbing, governance, and observability than on model choice—invest in engineering first.'

'Explore five no-code platforms that simplify building AI assistants, RAG systems, model tuning, and agent workflows in minutes'

'IBM published two ModernBERT-based Granite R2 embeddings offering 8k context, compact architectures, and high retrieval throughput suitable for production RAG and search systems.'

'Meta Superintelligence Labs released REFRAG, a decoding framework that compresses retrieved passages to enable 16× longer contexts and up to 30.85× faster time-to-first-token while preserving accuracy.'

'Google released EmbeddingGemma, a 308M-parameter on-device embedding model that tops MTEB scores for models under 500M and delivers low-latency multilingual retrieval suitable for offline RAG.'

DeepMind demonstrates a mathematical limit on fixed-size dense embeddings that causes retrieval failures in RAG systems at scale, and the LIMIT benchmark exposes this ceiling even on small toy tasks.

'Learn how to build an AI agent that summarizes recent conversations for short-term context and stores distilled facts in a FAISS-backed vector memory for long-term recall.'

'Discover when to use tokenization versus chunking to balance model efficiency, cost, and context preservation in AI applications.'

'For banks and insurers in 2025, prefer SLMs for latency-sensitive extraction and internal workflows and reserve LLMs for long-context synthesis and complex multi-step reasoning; governance and NIST-aligned controls are mandatory.'

'BlackRock's AlphaAgents splits equity research across specialized LLM agents to combine fundamentals, sentiment, and valuation for improved portfolio outcomes and risk control.'

'Eleven essential concepts every enterprise leader should master to move AI initiatives from pilots to scalable production, focusing on integration, data, trust, and process redesign.'

'NuMind launched NuMarkdown-8B-Thinking, a reasoning-first OCR VLM that infers layout and outputs clean Markdown ideal for RAG and document archiving.'

'Graph-R1 combines hypergraph knowledge, agentic multi-turn retrieval, and end-to-end RL to deliver state-of-the-art QA accuracy and efficient generation.'

'A concise 2025 guide to AI agents covering what they are, where they work reliably, risks, architecture patterns, and evaluation strategies.'

EraRAG introduces a scalable retrieval framework optimized for dynamic, growing datasets by performing efficient localized updates on a multi-layered graph structure, significantly improving retrieval efficiency and accuracy.

MMSearch-R1 introduces a reinforcement learning framework that enables large multimodal models to perform efficient, on-demand searches by learning when and how to retrieve relevant information, significantly improving accuracy and reducing search overhead.

Context engineering enhances AI performance by optimizing the input data fed to large language models, enabling more accurate and context-aware outputs across various applications.

WebThinker is a new AI agent that empowers large reasoning models to autonomously search the web and generate detailed scientific reports, significantly improving performance on complex reasoning benchmarks.