Rag AI News & Research · DeepTrendLab

Hybrid Search and Re-Ranking in Production RAG

When semantic search isn't enough for the RAG The post Hybrid Search and Re-Ranking in Production RAG appeared first on Towards Data Science .

Proxy-Pointer Framework for Structure-Aware Enterprise Document Intelligence

Hierarchical understanding and comparison of contracts, research papers, and more The post Proxy-Pointer Framework for Structure-Aware Enterprise Document Intelligence appeared first on Towards Data Science .

"OncoAgent: A Dual-Tier Multi-Agent Framework for Privacy-Preserving Oncology Clinical Decision Support"

RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production

Three weeks into testing, a learner told me my AI tutor gave her the wrong answer. Not obviously wrong — just outdated enough to mislead. That was the moment I…

Give Your AI Unlimited Updated Context

The architecture behind a portable knowledge layer and the automation that keeps it alive. The post Give Your AI Unlimited Updated Context appeared first on Towards Data Science .

Gemini API File Search: The Easy Way to Build RAG

Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing…

RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time

Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach…

Single Agent vs Multi-Agent: When to Build a Multi-Agent System

A practical guide to understanding AI agent design, ReAct workflows, and when to scale from a single agent to a multi-agent system. The post Single Agent vs Multi-Agent: When to…

Agentic RAG Explained in 3 Levels of Difficulty

Traditional

MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG

Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when…

Effective KV Compression with TurboQuant

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines —…

Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It

As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a failure that most monitoring systems never detect. This article walks through a reproducible experiment showing…

5 Techniques for Efficient Long-Context RAG

Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System

Multimodal Embedding & Reranker Models with Sentence Transformers

Build a Domain-Specific Embedding Model in Under a Day

Do text embeddings perfectly encode text?

'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.