Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But similarity is a weak proxy for what we actually need: relevance grounded in reasoning. In long, professional documents—like financial reports, research papers, or legal texts—the right answer […]
The post RAG Without Vectors: How PageIndex Retrieves by Reasoning appeared first on MarkTechPost.
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Not obviously wrong — just outdated enough to mislead.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 realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw.
The fix wasn’t in the retriever or the model. It was in the gap between them.
I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches.
The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.
RAG is a model that connects large language models to live agency knowledge bases — enabling grounded, mission-specific responses, rather than generic outputs.
For all their technical capabilities, large language models (LLMs) still have a memory problem. They can lack the ability to retain context across conversations, and don’t always contain the frameworks to let them access relevant data, ultimately making their results unreliable and untrustworthy.
NoSQL database pioneer MongoDB is taking on this problem, releasing new persistent memory, retrieval, embedding, and re-ranking features, all integrated into one platform. The company is also introducing new security connectivity, open-source plugins, and other framework integrations to support agentic AI workloads.
Supporting agentic memory
“Unlocking the power of agents requires memory,” Pete Johnson, MongoDB’s field CTO of AI, said during a press briefing. “Just like human memory, a good agentic memory organizes knowledge. It helps agents retrieve the right knowledge based on context and learn to make smarter decisions and take optimized actions over time.”
To advance automated retrieval an
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 are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single […]
The post Gemini API File Search: The Easy Way to Build RAG appeared first on Analytics Vidhya.
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 users.
The post RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time appeared first on Towards Data Science.
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 building assistants that need both historical context and a deeper understanding of users. MemPalace offers a different approach, enabling structured, persistent memory with higher precision […]
The post MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG appeared first on Analytics Vidhya.
A few years ago, most AI models ran out of context after a short conversation. Today, leading models hold one million tokens or more. This guide breaks down context length in LLMs, how tokens work, what the lost in the middle effect means for output quality, and when RAG outperforms long context.
