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javacodegeeks.com /2 weeks ago

Improving RAG Retrieval with Contextual Embeddings and Hybrid Search

Retrieval-Augmented Generation (RAG) has reshaped how modern AI systems are designed by allowing language models to access external knowledge at runtime. Instead of relying solely...

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habr.com /1 week ago

Contextual Retrieval: техника, которая чинит главную проблему RAG за 50 центов на тысячу чанков

Классический RAG часто ошибается не из‑за слабой embedding‑модели, а потому что чанки теряют связь с исходным документом. Разбираем, как Contextual Retrieval возвращает этот контек...

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towardsdatascience.com /3 weeks ago

Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG

Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is Filtering, Not Sear...

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medium.com /3 weeks ago

Retrieval Is the Product: BM25, Embeddings, and the Hybrid Default

Rather than treating retrieval as a fixed recipe, in this blog we derive it from first principles. We explore why BM25 looks the way it…Continue reading on Medium »

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medium.com /3 weeks ago

How Retrieval Systems Power Modern RAG Applications

How modern AI systems overcome hallucinations and outdated knowledgeContinue reading on Medium »

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machinelearningmastery.com /1 month ago

Building Context-Aware Search in Python with LLM Embeddings + Metadata

Keyword search breaks the moment a user types something a document doesn't literally say.

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dzone.com /1 month ago

Reducing RAG Hallucinations With Relationship-Aware Retrieval

Retrieval-augmented generation (RAG) is now the default pattern for grounding large language models in private or domain-specific knowledge. Yet most RAG systems still hallucinate,...

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previousnext.com.au /1 month ago

PreviousNext: Keywords to Context: Semantic Search and Retrieval-Augmented Generation with OpenSearch

Keyword search struggles with natural language and exploratory questions. Daniel walked the DrupalSouth 2026 audience through how OpenSearch and Skpr enable semantic search that un...

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dzone.com /1 month ago

RAG Is Not Enough: Advanced Retrieval Architectures Using Vertex AI Search on GCP

Retrieval-augmented generation (RAG) caught on fast — and for good reason. Connecting a large language model to your organization's documents feels like the most natural way to bui...

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venturebeat.com /1 month ago

Your AI agents need a terminal, not just a vector database

When agentic workflows fail, developers often assume the problem lies in the underlying model’s reasoning abilities. In reality, the limited information provided by the retrieval i...

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venturebeat.com /1 month ago

Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits

Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: prod...

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machinelearningmastery.com /1 month ago

Implementing Hybrid Semantic-Lexical Search in RAG

Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-rea...

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pub.towardsai.net /2 weeks ago

When Does HyDE Help RAG? I Tested 3 Query Types and It Failed on Two

I tested HyDE Retrieval vs Standard vector search on semantic, proprietary, and keyword queries. HyDE won one and quietly made 2 worse. Continue reading on Towards AI »

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towardsdatascience.com /2 weeks ago

Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory

I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval. The p...

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towardsdatascience.com /2 weeks ago

Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer

Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on on...

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dzone.com /3 weeks ago

Connect Existing Data to AI Retrieval: How to Build Production-Ready Search Without Rebuilding Core Systems

Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Cognitive Databases, Intelligent Data: Unified Infrastructure for Vector Search,...

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venturebeat.com /2 weeks ago

New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

Long-horizon reasoning exposes a core weakness in AI agents: context windows fill up fast, and retrieval pipelines return noise instead of signal.To solve this, researchers at the...

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towardsdatascience.com /2 weeks ago

An LLM as arbiter in RAG retrieval: picking the right candidate with reasons

Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend The post An LLM as arbiter in...

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towardsdatascience.com /6 days ago

Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents

Enterprise Document Intelligence [Vol.1 #7quater] - A 492-page document has a 358-entry table of contents. You can’t read it all, and top-k over every page mixes the answer with it...

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nngroup.com /1 month ago

Context Architecture

Context architecture applies information architecture principles to AI systems, helping agents interpret information and produce better, user aligned responses.

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habr.com /3 weeks ago

Retrieval в 2026: как RAG переехал с энкодеров на LLM (и что с этим делать в своём проекте)

Если вы строили RAG в 2023, ваш стек выглядел плюс-минус одинаково. BERT-семейство (BGE, e5) для семантики, BM25 для буквальных совпадений, cross-encoder для реранкинга, какой-нибу...

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towardsdatascience.com /1 week ago

Long Context vs. Short Context Model: When Does a Long Context Model Win?

Balancing context capability against cost, speed, and data The post Long Context vs. Short Context Model: When Does a Long Context Model Win? appeared first on Towards Data Science...

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venturebeat.com /1 month ago

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...

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towardsdatascience.com /1 week ago

The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation

Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Untaught Lessons of RAG...

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